The 39th Annual AAAI Conference on Artificial Intelligence
February 25 – March 4, 2025 | Philadelphia, Pennsylvania, USA
AAAI-25 Workshop Program
Sponsored by the Association for the Advancement of Artificial Intelligence
March 3-4, 2025 | Pennsylvania Convention Center | Philadelphia, Pennsylvania, USA
W1: Translational Institute for Knowledge Axiomatization (TIKA)
W4: AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention
W6: AI Governance: Alignment, Morality, and Law
W7: AI to Accelerate Science and Engineering
W8: AI4EDU: AI for Education: Tools, Opportunities, and Risks in the Generative AI Era
W9: Artificial Intelligence for Cyber Security (AICS)
W10: Artificial Intelligence for Music
W13: Economics of Modern ML: Markets, Incentives, and Generative AI
W14: Preparing Good Data for Generative AI: Challenges and
Approaches (Good-Data)
W15: Innovation and Responsibility for AI-Supported Education
W17: Planning in The Era of Large Language Models
W18: Post-Singularity Symbiosis: Preparing for a World with Superintelligence
W19: Preventing and Detecting LLM Generated Misinformation
W20: Privacy-Preserving Artificial Intelligence
W21: Quantum Computing and Artificial Intelligence (QC+AI)
W22: Web Agent Revolution: Enhancing Trust and Enterprise-Grade Adoption Through Innovation
W23: Imageomics: Discovering Biological Knowledge from Images Using AI
W24: Workshop on Datasets and Evaluators of AI Safety
W25: Workshop on Document Understanding and Intelligence
W26: Workshop on Multi-Agent Path Finding
W27: Foundation Models for Biological Discoveries (FMs4Bio)
W28: Advancing LLM-Based Multi-Agent Collaboration
W29: AI Agent for Information Retrieval: Generating and Ranking
W30: AI4Research: Towards a Knowledge-grounded Scientific Research Lifecycle
W31: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications
W32: Artificial Intelligence for Wireless Communications and Networking (AI4WCN)
W33: Artificial Intelligence with Causal Techniques
W34: Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
W35: CoLoRAI – Connecting Low-Rank Representations in AI
W36: Computational Jobs Marketplace
W37: DEFACTIFY 4.0 – Workshop Series on Multimodal Fact-Checking and Hate Speech Detection
W38: FLUID: Federated Learning for Unbounded and Intelligent Decentralization
W39: Generalization in Planning
W40: Workshop and Challenge on Anomaly Detection in Scientific Domains
W41: Knowledge Graphs for Health Equity, Justice, and Social Services
W42: Large Language Model and Generative AI for Health
W43: Machine Learning for Autonomous Driving
W44: MALTA: Multi-Agent Reinforcement Learning for Transportation Autonomy
W45: Neural Reasoning and Mathematical Discovery — An Interdisciplinary Two-Way Street
W46: Open-Source AI for Mainstream Use
W47: Scalable and Efficient Artificial Intelligence Systems
W1: Translational Institute for Knowledge Axiomatization
Three critical revolutions are shaping the landscape of AI: deep learning, knowledge graphs and automated reasoning. While deep learning has unleashed powerful tools such as ChatGPT, knowledge graphs and automated reasoning are providing essential backbone services for major corporations through applications such as business intelligence and automated verification. The National AI Research Resource (NAIRR) is addressing all aspects of deep learning. There is, however, no similar resource pooling for knowledge graphs and automated reasoning. The goal of this workshop is to catalyze a global effort to fill this gap by creating a Translational Institute for Knowledge Axiomatization (TIKA). TIKA is envisioned to serve as a hub for research, education, training, and hosting of repositories of open-source knowledge based on open data sources. The institute would focus on “use-inspired” research, addressing pragmatic issues in translating advances in knowledge graphs and reasoning to practice.
Topics of Interest
TIKA is envisioned to consider explicit knowledge in all forms including structured data, rules, controlled languages, mathematical formulas, etc. While AI for science must incorporate domain intelligence, it should also include knowledge to enable common sense reasoning and deep inferential reasoning that commercial knowledge graphs do not do today. The objectives of TIKA would be to pursue more human like learning that would be much more efficient and would not require as much training data and effort as the current generation of deep learning. This form of learning has also been referred to as “learning by being told” in the sense of McCarthy’s advice taker. TIKA is to advance the state-of-the-art in knowledge axiomatization by taking into account experience from prior efforts such as Cyc, Project Halo, Wolfram Alpha, ProtoOKN and the Linguistic Data Consortium. As the goal of the workshop is to serve as a launchpad for TIKA with input from the broader AI community, we welcome submissions on topics that will constructively advance this mission. Some (but not all) of the topics include:
- Use Cases for Knowledge Axiomatization
- Educational Resources for TIKA
- Technical Resources for TIKA (e.g., computational representations, ontologies, domain models, reasoners, etc.)
- Approaches to transition from domain-specific (little semantics) to broader, more generalized systems (big semantics)
- Semantic Challenges in Enterprise Data Architectures
- Impact and Legacy of Cyc including lessons for TIKA
- Technical Roadmaps for TIKA including research challenges
- Turing Tests for Knowledge Axiomatization
Workshop Format
The workshop will take place over two days, featuring a mixture of invited talks, panel discussions, and contributed presentations.
Attendance
Attendance is based on invitation.
Submission Guidelines
We welcome submissions of original research papers (8 pages) as well as short papers (4-6 pages) focused on case studies, work-in-progress, or visionary ideas. All submissions must adhere to the AAAI author guidelines. Submissions should be made through the workshop’s OpenReview.Net portal. For any questions, please write to us at tika-2025@googlegroups.com.
Important Dates
Submission Deadline: November 24, 2024
Notification of Acceptance: December 9, 2024
Workshop Date: March 3-4, 2025
Organizing Committee
Vinay Chaudhri (RelationalAI) – vchaudhri@acm.org
Chaitanya Baru (National Science Foundation) – cbaru@nsf.gov
Michael Genesereth (Stanford University) – genesereth@stanford.edu
Michael Witbrock (University of Auckland) – m.witbrock@auckland.ac.nz
Workshop Website:
W2: Advancing Artificial Intelligence through Theory of Mind (ToM4AI): Bridging Human Cognition and Artificial Intelligence
Theory of Mind, the ability to reason about and attribute mental states—such as beliefs, intentions, desires, and emotions—to oneself and others, is essential for predicting behaviour. As artificial intelligence (AI) systems evolve towards greater interactivity, ToM principles enable better interpretations and responses of human actions and intentions to provide the equivalent computational efficiency humans enjoy.
This workshop will convene researchers from diverse fields, ranging from psychology, philosophy, cognitive science, neuroscience, robotics, and AI, to explore the implications of ToM in developing advanced AI systems. We aim to bridge the gap between theory-driven cognitive science and practical AI applications, fostering a multidisciplinary dialogue on the role of ToM in AI.
The outcomes of this workshop will contribute to the theoretical understanding of ToM in AI and inspire new research directions, collaborations, and an interdisciplinary community focused on this topic.
We invite the participation of researchers who are interested in directly addressing Theory of Mind in AI. We are also expecting submissions to ToM4AI and related topics relevant to designing, developing, demonstrating, or evaluating ToM-based AI agents.
Topics
Topics include, but are not limited to:
- Developmental ToM: Insights for AI
- The Role of ToM in Human-AI Interaction
- Computational ToM
- ToM in Large Language Models: Emulation, Emergence, or Anthropomorphic Illusion?
- Plan and goal recognition
- ToM in human-robot interactions
- Epistemic reasoning and planning
- BDI and adaptive cognitive architectures
- Multi-agent communication and meta-cognition
- Knowledge representation and meta-reasoning
- ToM evaluation and benchmarks
- Evolution and simulation of ToM
- Philosophical aspects of Artificial Mind-reading
Format
Papers should be submitted via OpenReview
We will accept submissions in the form of extended abstracts (up to 2 pages, references excluded) on the broader ToM4AI spectrum. Papers must be in high-resolution PDF format, formatted for US Letter (8.5″ x 11″) paper, using Type 1 or TrueType fonts. Reviews are double-blind, and submissions must conform to the AAAI-25 submission instructions found here (zip file is available on the workshop site:
The workshop is a 1 day event (date TBD), including 4 keynote talks (speakers listed below), paper presentation session and a round table session.
Planned speakers:
- Sheila McIlraith, Professor, Department of Computer Science, Toronto University
- Rebbeca Saxe, Professor, Department of Brain and Cognitive Sciences, MIT.
- Joshua Tenenbaum, Professor, Department of Brain and Cognitive Sciences, MIT.
- Rineke Verbrugge, Professor, Artificial Intelligence Bernoulli Institute, Uni. of Groningen.
Organizing Committee (alphabetized):
Nitay Alon, Department of Computer Science, The Hebrew University of Jerusalem (Primary POC: nitay.alon@mail.huji.ac.il)
Joseph Barnby, Cognitive Science at Royal Holloway, University of London
Reuth Mirsky, Department of Computer Science, Tufts university
Stefan Sarkadi, King’s College London
Workshop URL
W3: AI for Public Missions
The AI for Public Missions workshop aims to convene a community of scientists, engineers, and practitioners with public missions, to better leverage AI towards challenging problems of societal importance. This includes the efforts of federal, state, and local governments, as well as non-partisan non-governmental and community organizations.
As governments continue to leverage AI to achieve institutional goals, numerous hurdles are certain to emerge that limit its successful application. Publicly-funded research should ideally balance support for topics that address challenges that hinder AI serving public needs, in addition to commercial and industry settings.
This event will create a unifying venue between stakeholders to help understand real world challenges and advance potential solutions. It aims to cross between use-inspired foundational research, applied research, and case-studies that document successful processes by which AI has been deployed and responsibly governed.
Topics
Both technical and operational submissions are encouraged around the topic areas. Technical topics include the science, engineering, and processes relevant to the development and deployment of AI for the uniquely complex applications and use-cases found in public service and government environments. Operational topics address the business use cases, workflows and applications of potential public service activities for which AI might have a transformational impact.
- Improving data to empower AI for public service:
- Data integrity, provenance and quality;
- Publicly available datasets.
- Ensuring AI systems serve all communities without bias and error:
- Testing, Evaluation, and Verification and Validation;
- Self-updating models.
- Building AI that empowers the public-serving workforce:
- Trustworthy AI;
- Human-centered AI;
- AI training and workforce development.
- Creating new AI capabilities for public missions.
- Government initiatives in AI and Autonomy;
- Challenge problems and grand challenges.
Format
Workshop activities will include keynote talks, panels, and lighting presentations. Depending on the extent of government and NGO attendance, breakout sessions might be added. The conference also seeks to strengthen norms around the application of responsible research practices, such as risk-mitigation efforts and safety- or rights- impact statements.
Submission Requirements
Two submission options seek to enable government groups and NGOs to present current work, though they might not have the ability to publish technical papers. Submission information can be found on the workshop webpage.
To foster dialogue between academic and public mission groups, all submissions should include an “Application Context” section of 1-2 paragraphs addressing relevant items listed below.
For submissions on operational topics (see definition in Topics):
- What is trying to be accomplished
- How it is done today
- What technologies have been brought to bear already if any
- What new capabilities or functionality are needed
- What kind of data or feedback might be available to leverage.
For submissions on technical topics (see definition in Topics):
- What does it do?
- What is an application that it might be used for?
- What applications has it been used for ?
- What attributes or properties does it have that might make it a good/bad fit for specific public missions?
Academic researchers are welcome to submit:
- Technical papers: Full-length research papers of up to 7 pages (excluding references and appendices).
- Short papers: research/position papers of up to 4 pages (excluding references and appendices).
Government/NGOs are welcome to submit:
- Proposals for lighting session presentation on work of government body or organization: 1-page limit.
- Proposals for breakout sessions: 2-page limit.
- Poster session tabling activities (laptops with slides / hands-on demonstrations): 2-page limit.
- Abstracts or short papers: Research or position papers: 4-page limit (excluding references and appendices).
Important Dates
- November 24 – Workshop Submissions Due to Workshop Organizers
- December 9 – Organizers send acceptance/rejection letters to participants, including allotted page lengths.
- March 3 or 4 (TBD) – Workshop held in Philadelphia, at AAAI 39
Workshop Website:
https://sites.google.com/view/ai-public-missions-workshop
Workshop Committee:
Please send workshop related questions to: publicmissionAI@gmail.com
William Regli, Professor at UMD College Park (co-chair)
Avital Percher, Health Innovation Advisor, Avantiqor. Former Responsible AI Official, NSF. (co-chair)
Michael Littman, Director, Division of Information and Intelligent Systems, NSF
Rob Reich, Associate Director, Stanford Institute for Human-Centered AI, Senior Advisor to the US AI Safety Institute
Jaret C. Riddick, Senior Fellow at Georgetown University’s Center for Security and Emerging Technology (CSET). Former Principal Director for Autonomy in the Office of the Under Secretary for Research and Engineering (OUSD(R&E))
Adam Russell, Director, Artificial Intelligence Division, USC Information Sciences Institute
Wade Shen, Director, Proactive Health, ARPA-H Former Deputy Chief Technology Officer and Director of the National AI Initiatives Office, OSTP
Milind Tambe, Director, Center for Research on Computation and Society at Harvard University
W4: AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention
The rapid expansion of artificial intelligence (AI) solutions across various sectors has opened up unprecedented opportunities and challenges, particularly in the realms of finance, social media, and crime prevention. The “AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention” workshop aims to explore the transformative potential of AI in fostering socially responsible practices and ensuring ethical standards across these interconnected domains.
This workshop will delve into the latest advancements in AI technologies that are driving social impact in the financial services industry, including the integration of Environmental, Social, and Governance (ESG) factors into investment decisions, combating financial crimes, and promoting financial inclusion. Additionally, the workshop will address the critical role of AI in safeguarding social media platforms from manipulation and misinformation, as well as its applications in crime prevention and public safety.
Key themes of the workshop include responsible AI practices, safety protocols, and ethical considerations, with a particular focus on model safety and the prevention of unintended consequences such as bias in AI-driven decision-making. Through a series of keynotes, panels, invited talks, paper presentations, and poster sessions, participants will have the opportunity to engage in cross-disciplinary discussions, share innovative ideas, and collaborate on solutions to current challenges.
Topics for contributed papers should lie at the intersection of AI, Finance/Social Media, and Social Good.
Topics
Topics include, but are not limited to:
- Generative Models and Data-Driven Simulation
- Planning, Search, Constraint-Based Reasoning, Optimization, and Reinforcement Learning
- Multi-Agent Systems and Game Theory
- Transformer Models, Self-Supervised Learning
- Natural Language Processing, Including Large Language Models (LLMs), Speech Analysis, and Conversational Dialogue Modeling
- Meta Learning, Federated Learning, Representation Learning, Causal Learning, and Transfer Learning
- Computer Vision
- Graph Theory and Network Analysis
- Data Annotation, Acquisition, Augmentation, and Feature Engineering
- Semi-Structured Data Modeling
- Model Validation and Calibration
- Ethical AI Capabilities and Challenges
- AI Safety Protocols and Processes (Especially for High-Risk Industries and Applications)
- Role of Model Governance in Regulating GenAI
- Advanced AI Monitoring and Regulation Capabilities for GenAI
Potential applications of interest in Finance / Social Media and Social Good may include but are not limited to:
- Combating Financial Crime Including Fraud Detection
- Developing and Implementing AI Solutions for ESG (Environmental, Social, and Governance) Investing
- Data Privacy
- Financial Safety and Education for Vulnerable and/or Underrepresented Populations
- Decentralized Finance (DeFi) Frameworks and Benchmarks
- Bias Analysis and Mitigation in AI Models for Financial Decision Making
- Explainability and Fairness for Financial AI and ML Systems
- High-Risk Use of LLMs in Key Industries
- Emerging Use of LLMs in Crimes and Potential Solutions
- Systemic Risks LLM Capabilities Pose to Critical Industries
- Misinformation Detection and Mitigation in Social Media
- Content Moderation in Social Media
- User Behavior Analysis
Format of Workshop:
The workshop will follow a one-day format, focusing on paper presentations, poster sessions, panels, keynote and invited talks.
Attendance:
We anticipate attracting a minimum of 100 and potentially up to 150 attendees.
People who have been accepted to present papers and posters are invited to attend. In addition, anyone interested in AI, Finance and Social good are welcome to attend.
Submission Requirements:
All contributions must be original, unpublished and should not be under consideration by other conferences or journals. Submissions will undergo a peer review process using a double-blind system. The evaluation criteria include relevance to the workshop, novelty, technical contribution, impact significance, clarity, and reproducibility. To ensure consistency, all submissions must adhere to the AAAI-25 formatting guidelines and utilize the corresponding LaTeX style files.
Two types of submissions are accepted:
- Full research papers, which should not exceed 8 pages (including references)
- Short/poster papers, which should not exceed 4 pages (including references)
The submission process will take place via Microsoft CMT – https://cmt3.research.microsoft.com/AAAIAISI2025/Track/1/Submission/Create
Accepted papers require in-person presentation by at least one author. All accepted papers will be published on the workshop website, and authors are encouraged to also share their work on platforms like arXiv or other online repositories. If you have any queries regarding the submission process, please contact us at aaai-2025-aisi@googlegroups.com.
Important Dates
- Submission deadline: Sunday, November 24, 2024 (anywhere on earth)
- Notification of decision: Monday, December 9, 2024
Workshop Chairs:
Ferdinando Fioretto, University of Virginia, fioretto@virginia.edu
Dhagash Mehta, BlackRock, dhagashbmehta@gmail.com
Workshop Organizing Committee:
- Suchetha Siddagangappa, J.P. Morgan AI Research, suchetha.siddagangappa@jpmchase.com
Rachneet Kaur, J.P. Morgan AI Research, rachneet.kaur@jpmchase.com Kassiani Papasotiriou, J.P. Morgan AI Research, kassiani.papasotiriou@jpmchase.com - Nazanin Mehrasa, Borealis AI, Royal Bank of Canada, nazanin.mehrasa@borealisai.com
- Alberto Pozanco Lancho, J.P. Morgan AI Research, alberto.pozancolancho@jpmorgan.com
- Ani Calinescu, Oxford University, ani.calinescu@cs.ox.ac.uk
- Eren Krushan, Morgan Stanley/Princeton, erenkurshan@gmail.com
Technical Program Committee:
- Naftali Cohen, Schonfeld/Columbia, naftalic@gmail.com
- Yu Yu, Blackrock, Yu.Yu@blackrock.com
- Christopher Policastro, New York University, christopher.policastro@nyu.edu
- Elaine Shi, Carnegie Mellon University, rshi@andrew.cmu.edu
- Tucker Balch, Emory University, tucker.balch@emory.edu
- Brian Barr, Capital One, brian.barr@capitalone.com
- Yongjae Lee, Ulsan National Institute of Science and Technology, yongjaelee@unist.ac.kr
- Bo An, Nanyang Technological University, boan@ntu.edu.sg
- Eric Jiawei He, Borealis AI, Royal Bank of Canada, eric.j.he@borealisai.com
- Senthil Kumar, Capital One, senthil.kumar@capitalone.com
Website URL
W5: AI for Urban Planning
The 1st Workshop on AI for Urban Planning aims to bring together researchers, practitioners, and policymakers to explore innovative AI-driven solutions for the multifaceted challenges in urban planning. As cities face rapid urbanization and the pressing need for sustainability, there is an urgent demand for advanced strategies in better land utilization, resource optimization, infrastructure development, improved equity, data-driven policymaking, as well as the better understanding of complex interdependencies between physical and virtual spaces. This workshop will delve into the latest AI technologies to build sustainable, diverse, and human-centric cities of the future.
Topics
Topics of Interest
We encourage submissions on a broad range of topics related to AI in urban planning, including but not limited to:
- Representation and Quantification of Urban Environments
- Representation learning for spatio-temporal data
- Multimodal data fusion
- Graph neural networks in urban form representation
- Domain shift and generalization in urban data
- Human Dynamics for Urban Planning
- Reconceptualizing Human Behavior Using AI Models
- Human-Robot/AI Interactions
- Exploring Human Dynamics Beyond Physical Spaces through AI and Virtual Environments
- Predictive Modeling and Forecasting
- Urban time series forecasting
- Spatio-temporal forecasting (e.g., energy, traffic, crowd flow)
- Demographic and climate modeling
- Generative Modeling and Large AI Models for Urban Planning
- Urban form and land use configuration
- Transportation system design
- Architecture and landscape design
- Evaluation and Simulation
- Reinforcement learning-powered simulations
- LLM agents for evaluation and stakeholder role-play
- Human-in-the-loop simulations
- Ethics and Fairness in AI for Urban Planning
- Fairness, bias, transparency, and accountability
- Community engagement and data privacy
- Applications, Systems, and Tools
- Urban digital twins, AR/VR in urban planning
- Planning support systems for policymakers
- Carbon neutrality and utility/resource allocation
Format of Workshop
The workshop will follow a half-day format, focusing on paper presentations, poster sessions, keynote and invited talks.
Attendance
We anticipate attracting a minimum of 75 and potentially up to 100 attendees.
People who have been accepted to give paper presentations, posters are invited to attend. In addition, the workshop is open to researchers, industry professionals, and policymakers interested in AI applications for urban planning. Due to venue capacity, attendance may be limited.
Submission Requirements
We welcome full-length papers, work-in-progress, extended abstracts, and postition papers. Papers should be submitted in the AAAI format. Papers previously submitted to other journals / conferences are welcome. We accept both short papers with no more than 4 pages and long papers with no more than 8 pages. Each submission will undergo a double-blind peer-review process. Papers accepted by the workshop will not be published but only for demonstration in the workshop.
Submission Site
The workshop uses EasyChair for paper submission and review. Here is the submission link: https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dai4up&d=DwIDaQ&c=KXXihdR8fRNGFkKiMQzstpt6drHDqSenG-8Qi3URQqo&r=dqOCds4_f2zlVi6Xdwtrktbt-Pqh2embh69jsy91Jtw&m=m89fv66iSAlJEXJh8veoLm5Mhoy7bblTz0w1sKJ_IroNH4LfITnoUdLKqoPNKDze&s=ba3W6wB0YgEJ2Bh8jwAi9GwdI_fYjD-tHyaXMd7M-iA&e=
Workshop Chair
- Pengyang Wang (University of Macau) – pywang@um.edu.mo
Workshop Committee
- Steven Jige Quan (Seoul National University) – sjquan@snu.ac.kr
- Xinyue Ye (Texas A&M University) – xinyue.ye@tamu.edu
- Hui Xiong (Hong Kong University of Science and Technology) – xionghui@hkust-gz.edu.cn
Workshop Website
W6: AI Governance: Alignment, Morality, and Law
The rapid advancements in Artificial Intelligence (AI) bring unprecedented opportunities and challenges. As organizations and governments increasingly integrate AI technologies into various aspects of society, the need for effective AI governance becomes paramount. This workshop aims to provide a comprehensive understanding of AI governance principles, frameworks, and best practices to empower participants in navigating the ethical, legal, and societal dimensions of AI.
In addition to insightful technical discussions, we also plan to hold introductory talks to educate a broader community about the importance and urgency of AI governance. These talks will serve as a foundational introduction to the subject matter and its implications for society at large.
AI governance is a critical field with two distinct communities: policymakers crafting regulations and researchers/developers working on the technologies. The workshop aims to bring these communities together to foster collaboration and enhance understanding. The workshop aims to educate a broader community about the intricacies of AI governance. Through informative sessions and discussions, participants will gain a deeper understanding of the challenges posed by AI technologies and the need for effective governance. We invite submissions of papers, posters, and demonstrations on any topic related to AI Governance, including but not limited to:
- Understanding AI Governance: Define the core principles of AI governance. Explore existing regulations and ethical frameworks
- Role of LLMs in AI Governance: Highlight the capabilities of LLMs in analyzing and generating human-readable content. Discuss how LLMs can contribute to drafting policies and guidelines.
- Addressing Bias and Fairness: Examine the challenges of bias in AI systems. Showcase how LLMs can assist in identifying and mitigating bias.
- Transparency and Accountability: Explore the importance of transparency in AI decision-making. Discuss how LLMs can aid in creating understandable and accountable AI systems.
- Policy Interpretation and Compliance: Illustrate how LLMs can assist policymakers in understanding complex technical concepts. Discuss the role of LLMs in monitoring and auditing AI systems for compliance with governance standards.
- Human-AI Collaboration: Emphasize the need for human oversight in AI governance. Discuss the collaborative approach between humans and LLMs.
- AI alignment methods
- Prompt engineering
- Fine tuning
- Explainable AI
- Responsible AI practices
We welcome submissions from researchers working on related topics such as neural program synthesis, program induction, and concept learning.
Format of Workshop:
Submissions should be in the form of a 7-page papers (+2-page references) for proceeding articles or a 2-page abstract for posters/demonstrations, and should be formatted according to the conference’s guidelines (https://www.ijcai.org/authors_kit). Accepted papers will be presented at the workshop and included in a workshop proceeding, unless opted out by the authors.
The review will be double-blind, so please keep your submission(s) anonymous. The accepted papers have the option to be archival (i.e. included in the proceeding) or non-archival (i.e. only hosted in the website). The archival papers should be sufficiently original and not published in another venue or journal. The non-archival papers can be relevant work presented or published in another venue or journal.
All accepted papers and extended abstracts will be presented as posters. The program committee will select a few papers for oral presentation. There will be a poster session during the scheduled coffee breaks to facilitate and spark discussions amongst attendees.
We look forward to your submissions and to seeing you at the workshop. If you have any questions, please feel free to contact the organizing committee.
Confirmed Keynote and Invited Speakers:
Kush Varshney | IBM Research
Francesca Rossi |IBM Research
Kartik Talamadupula | Symbl.ai
Organizing Committee
Baihan Lin |Columbia University, Mount Sinai
Djallel Bouneffouf | IBM Research
Asim Munawar | IBM Research
Irina Rish | Mila – Quebec AI Institute
Lauri Goldkind | Fordham University
Technical Program Committee (TPC)
We would like to express our sincere gratitude to our technical program committee for generously volunteering their time and expertise to review submissions for our workshop. Their valuable contributions have been instrumental in ensuring the quality and rigor of the workshop’s program. We deeply appreciate their dedication and commitment to our workshop’s success:
Sonali Son, Yohan Mathew, Arian Khorasani, Ashraf Abdul, Serge Stinckwich, Giandomenico Cornacchia, Jawad Haqbeen, Chi Xie, Xiaoxia Lei, Lisa Lehmann, Prithviraj (Raj) Dasgupta, Sray Agarwal, Moncef Garouani, Chemlal Yman, Imran Nasim, Fakhare Alam, Adnan Zaidi, Zahid Farid, Pengwei Li, Manish Nagireddy, Jesus Rios Aliaga, Miao Liu, Muneeza Azmat, Kinjal Basu, Nudrat Nida, Inas Bachiri, Sarathkrishna Swaminathan
Website URL:
W7: AI to Accelerate Science and Engineering (AI2ASE)
Scientists and engineers in diverse application domains are increasingly relying on using computational and artificial intelligence (AI) tools to accelerate scientific discovery and engineering design. AI, machine learning, and reasoning algorithms are useful in building models and decision-making towards this goal.
We have already seen several success stories of AI in application domains such as materials discovery, ecology, wildlife conservation, and molecule design optimization.
This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals:
- Identify and understand the challenges in applying AI to specific science and engineering problems.
- Develop, adapt, and refine AI tools for novel problem settings and challenges.
- Community-building and education to encourage collaboration between AI researchers and domain area experts.
Format of the Workshop
The workshop schedule will include a series of invited talks, contributed talks, panel discussion, breakout session, and poster session.
Length of the workshop: 1 day
Invited Speakers
This year’s theme is AI for Biological Sciences. Our invited speakers, who are working on important problems at the intersection of AI and Biology, include:
- Prof. Caroline Uhler, Massachusetts Institute of Technology/Broad Institute
- Prof. Marinka Zitnik, Harvard University
- Prof. Alexis Battle, Johns Hopkins University
- Prof. Le Song, Mohamed bin Zayed University of Artificial Intelligence/BioMap
- Prof. Romain Lopez, New York University
- Dr. Samuel Stanton, Genentech
Attendance
We invite everyone attending AAAI conference to join for the workshop. We believe this is an exciting problem space where promising technologies of our generation can help address most challenging problems of our society by accelerating scientific discovery and engineering design.
Submission Requirements
We welcome submissions of long (max. 8 pages), short (max. 4 pages), and position (max. 4 pages) papers describing research at the intersection of AI and science/engineering domains including chemistry, physics, power systems, materials, catalysis, health sciences, computing systems design and optimization, epidemiology, agriculture, transportation, earth and environmental sciences, genomics and bioinformatics, civil and mechanical engineering etc.
Submissions must be formatted in the AAAI submission format. All submissions should be done electronically via CMT.
Submission Site: https://cmt3.research.microsoft.com/AI2ASE2025
Workshop Chair
Aryan Deshwal, University of Minnesota, adeshwal@umn.edu
Workshop Organizing Committee
Aryan Deshwal, University of Minnesota, adeshwal@umn.edu
Jana Doppa, Washington State University, jana.doppa@wsu.edu
Vipin Kumar, University of Minnesota, kumar001@umn.edu
Carla Gomes, Cornell University, gomes@cs.cornell.edu
Syrine Belakaria, Stanford University, syrineb@stanford.edu
Workshop Website
W8: AI4EDU: AI for Education: Tools, Opportunities, and Risks in the Generative AI Era
The rapid advancement of generative AI technologies presents both unprecedented opportunities and significant challenges within the educational landscape. With tools like ChatGPT, DALL-E, and other generative models becoming increasingly sophisticated, educators have access to powerful resources that can enhance learning experiences, personalize education, and streamline administrative tasks. However, these advancements also bring forth critical issues such as ethical considerations, data privacy concerns, and potential biases. This workshop aims to explore how generative AI can be effectively and responsibly integrated into education, ensuring that its benefits are maximized while mitigating associated risks.
Topics
Topics of interest
We invite high-quality paper submissions of a theoretical and experimental nature on generative AI topics including, but not limited to, the following:
- Emerging technologies in education
- Evaluation of education technologies
- Immersive learning and multimedia applications
- Self-adaptive learning
- Individual and personalized education
- Intelligent learning systems
- Intelligent tutoring and monitoring systems
- Automatic grading and assessment
- Automated feedback and recommendations
- Big data analytics for education
- Analysis of communities of learning
- Course development techniques
- Data analytics & big data in education
- Mining and web mining in education
- Learning tools experiences and cases of study
- Social media in education
- Smart education
- Digital libraries for learning
- Knowledge management for learning
- Learning technology for lifelong learning
- Tracking learning activities
- Wearable computing technology in e-learning
- Smart classroom
- Dropout prediction
- Knowledge tracing
Format of Workshop
We will invite AIED enthusiasts from all around the world through the following three different channels: (1) We will invite established researchers in the AIED community to give a keynote talk that (a) describes a vision for bridging AIED communities; (b) summarizes a well-developed AIED research area; or (c) presents promising ideas and visions for new AIED research directions. (2) We will call for regular workshop paper submissions related to a broad range of AI domains for education. (3) We will host an AIED doctoral consortium that provides an opportunity for a group of Ph.D. students to discuss and explore their research interests and career objectives with a panel of established AIED researchers..
Attendance
We expect to attract around 75 attendees and 50 submissions. We are offering a small number of travel scholarships to promote attendance of unrepresented students/postdocs.
Submission Requirements
The workshop solicits 5-7 pages double-blind paper submissions (with unlimited references) from participants. Submissions of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. The format is the standard double-column AAAI Proceedings Style. All submissions will be peer-reviewed.
Submission Site Information. The submission AUTHOR KIT can be found at https://aaai.org/authorkit25/. To ensure a fair review process, all submissions will be evaluated through a double-blind review.
Workshop Website
https://ai4ed.cc/workshops/aaai2025; For any questions email us at zitao.jerry.liu@gmail.com.
Workshop Organization
Main contact: Zitao Liu (Guangdong Insititute of Smart Education, Jinan University); Organizing Committees: John Stamper (Carnegie Mellon University), Andrew M. Olney (University of Memphis), Tianqiao Liu (TAL Education Group), Qingsong Wen (Squirrel Ai Learning), Jiliang Tang (Michigan State University)
W9: Artificial Intelligence for Cyber Security (AICS)
The workshop will focus on the application of artificial intelligence (AI) to problems in cyber security. While AI has shown tremendous promise in enhancing human decision-making in cyber security and even automating critical security functions, the security of these AI-enabled systems themselves remains a vulnerable frontier. The workshop will address technologies and their applications in security, such as, machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning and human machine interactions.
This year the AICS workshop emphasis will be on the “Security of AI-enabled Systems,” focusing on the emerging threats targeting these technologies and the advanced techniques needed to safeguard them. Security of AI-enabled systems refers to the strategies, tools, and practices designed to protect them from various threats, including adversarial attacks, data breaches, and model manipulation.
Topics
Topics of interest include, but are not limited to:
- Security and Robustness of AI-enabled Systems
- AI in Cyber Security
- Game Theoretic and Multi-agent Security Approaches
- Human Behavioral Modeling
- Ethics, Privacy, and Governance
- (AI mentioned above encompasses learning, planning, optimization, generative AI, etc.)
Format of Workshop
The workshop will have two invited talks. We will host a series of short (15-20 minute) presentations of the papers accepted for this workshop. Finally, we will hold a moderated roundtable/panel discussion between members of the AI community on practical issues related to the use of AI and security.
The AICS workshop will be a one-day meeting, from roughly 9am to 5pm.
Attendance
The organizers do not impose any criteria to attend, other than what AAAI registration imposes. The maximum number of attendees is to be determined by the room size.
Submission Requirements
how many pages; list of relevant research
work/publications, paper or abstract
We accept only full-length papers (min of 6 pages, up to overall 8 pages in the AAAI25 format).
Submissions are not anonymized.
Submission Site Information
Please look at http://aics.site/AICS2025/cfp.html
Workshop Chairs
James Holt, holt@lps.umd.edu
Edward Raff, raff.edward@umbc.edu
Ahmad Ridley, National Security Agency, USA
Dennis Ross, Dennis.Ross@ll.mit.edu
Ankit Shah, ankit@iu.edu
Arunesh Sinha, arunesh.sinha@rutgers.edu
Allan Wollaber, Allan.Wollaber@ll.mit.edu
Workshop Committee
The Program Committee is still to be determined.
Workshop Website
W10: Artificial Intelligence for Music
This one-day workshop will explore the dynamic intersection of artificial intelligence and music. It will investigate how AI is transforming music creation, recognition, education, ethical and legal implications, as well as business opportunities, from composition to performance, production, collaboration, and audience experience. Participants will gain insights into the technological challenges and accomplishments in music and how AI can enhance creativity, enabling musicians and producers to push the boundaries of their art. We will discuss how AI tools assist in sound design, remixing, and mastering, allowing for new sonic possibilities and efficiencies in music production. Additionally, we’ll examine AI’s impact on music education and the careers of musicians, exploring advanced learning tools and teaching methods. AI technologies are increasingly adopted in the music and entertainment industry. The workshop will also discuss the legal and ethical implications, including questions of authorship, originality, and the evolving role of human artists in an increasingly automated world. This workshop is designed for AI researchers, musicians, producers, and educators interested in the current status and future of AI in music.
Format
This is a full-day workshop from 9AM to 5PM. The workshop will include invited speeches, paper presentations, and a panel discussion.
Attendance
- What are the criteria to be invited: The workshop has already invited the speakers that are experts in this topic. The workshop is open to any attendee.
- The expected attendance is 50.
- At this moment, there is no limit on the maximum number of attendees. The workshop organizers will communicate with the conference organizers if the number of attendees exceeds the expectation.
Submission Requirements
(maximum 6 pages.) Work in progress is welcome. Authors are encouraged to include descriptions of their prototype implementation. Conceptual designs without any evidence of practical implementation are discouraged. Topics of interest include, but are not limited to:
- AI-Driven Music Composition and Generation
- Practice and Performance
- Music Recognition and Transcription
- Applications in Sound Design
- Generated Videos to Accompany Music
- Generated Lyrics based on music
- Legal or Ethical Implications
- AI’s impacts on Musicians’ Careers
- Music Education
- Business Opportunities
- Music Datasets and Data Analysis
Paper Format: Please follow the format required by AAAI at https://aaai.org/conference/aaai/aaai-25/aaai-25-main-technical-call-for-papers/
Submission Site Information: https://cmt3.research.microsoft.com/AI4Music2025
Important Dates:
- Submission: November 22, 2024
- Notification of Acceptance: December 9, 2024
- Final Version: December 31, 2024
Accepted Papers will be posted at the workshop website.
Workshop Chair
Yung Hsiang Lu, yunglu@purdue.edu
Workshop Committee
- Kristen Yeon-Ji Yun, Purdue University, yun98@purdue.edu
- George K. Thiruvathukal, Loyola University Chicago, gthiruvathukal@luc.edu
- Benjamin Shiue-Hal Chou, Purdue University, chou150@purdue.edu
Workshop Website
https://ai4musicians.org/2025aaai.html
W11: Cooperative Multi-Agent Systems Decision-Making and Learning: Human-Multi-Agent Cognitive Fusion
This workshop focuses on the role of decision-making and learning in human-multi-agent cooperation, viewed through the lens of cognitive modeling. AI technologies, particularly in human-robot interaction, are increasingly focused on cognitive modeling, encompassing everything from visual processing to symbolic reasoning and action recognition. These capabilities support human-agent cooperation in complex tasks.
Natural agents, like humans, often make decisions based on a blend of biological, social, and cognitive motivations, as elucidated by theories like Maslow’s Hierarchy of Needs and Alderfer’s Existence-Relatedness-Growth (ERG) theory. On the other hand, the AI agent can be regarded as a self-organizing system that also presents various needs and motivations in its evolution through decision-making and learning to adapt to different scenarios and satisfy their needs.. Combined with AI agent capacity to aid decision-making, it opens up new horizons in human-multi-agent collaboration. This potential is crucially essential in the context of interactions between human agents and intelligent agents, when considering to establish stable and reliable relationships in their cooperation, particularly in adversarial and rescue mission environments.
This workshop will bring together researchers from multi-agent systems (MAS) and human-robot interaction (HRI) and aims to advance the field by exploring how cognitive science, mathematical modeling, statistical analysis, software simulations, and hardware demonstrations can help answer critical questions about decision-making and learning in these cooperative environments. The goal is to understand better how cognitive modeling can enhance cooperation between humans and AI agents, especially in complex, high-stakes scenarios.
Topics
We solicit contributions from topics including but not limited to:
- Human-multi-agent cognitive modeling
- Human-multi-agent trust networks
- Trustworthy AI agents in Human-robot interaction
- Trust based Human-MAS decision-making and learning
- Consensus in Human-MAS collaboration
- Intrinsically motivated AI agent modeling in Human-MAS
- Innate-values-driven reinforcement learning
- Multi-Objective MAS decision-making and learning
- Adaptive learning with social rewards
- Cognitive models in swarm intelligence and robotics
- Game-theoretic approaches in MAS decision-making
- Cognitive model application in intelligent social systems
Format of Workshop
The workshop will be a full-day workshop with a mix of keynotes, contributed talks, focused discussions, and poster sessions. The morning session will consist of talks and discussions about the challenges of decision-making and learning based on cognitive modeling at cooperative MAS and AI solutions’ vision. The afternoon session will consist of invited talks by various AI experts to discuss algorithmic approaches to various multi-agent/robot decision-making and learning problems and spark discussion on connecting AI technologies with real problems.
Attendance
The attendance is expected mainly from the AI and robotics community. However, researchers and practitioners whose research might apply to cooperative MAS decision-making and learning or who might be able to use those techniques in their research are welcome.
Submission Requirements
Submissions can contain relevant work in all possible stages, including those recently published work, is under submission elsewhere, was only recently finished, or is still ongoing. Authors of papers published or under submission elsewhere are encouraged to submit these papers or short versions (including abstracts) to the workshop, educating other researchers about their work, as long as resubmissions are clearly labeled to avoid copyright violations.
We welcome contributions of both short (2-4 pages) and long papers (6-8 pages) related to our stated vision in the AAAI 2025 proceedings format. Position papers and surveys are also welcome. The contributions will be non-archival but will be hosted on our workshop website. All contributions will be peer reviewed (single-blind).
Submission Site Information
Submission Link: https://easychair.org/conferences/?conf=aaai2025cmasdlworksh
Workshop Chairs:
- Qin Yang (Main Contact) | Email: RickYang2014@gmail.com | Affiliation: Computer Science and Information Systems Department, Bradley University
- Giovanni Beltrame | Email: giovanni.beltrame@polymtl.ca | Affiliation: Department of Computer Engineering and Software Engineering, Polytechnique Montreal
- Alberto Quattrini Li | Email: alberto.quattrini.li@dartmouth.edu | Affiliation: Computer Science Department, Dartmouth College
- Christopher Amato | Email: c.amato@northeastern.edu |Affiliation: Khoury College of Computer Sciences, Northeastern University
Workshop Committee
- Matthew E. Taylor | Email: matthew.e.taylor@ualberta.ca | Affiliation: Computer Science Department, University of Alberta
- Marco Pavone | Email: pavone@stanford.edu | Affiliation: Aeronautics and Astronautics Department, Stanford University
- Peter Stone | Email: pstone@cs.utexas.edu | Affiliation: Department of Computer Science, University of Texas at Austin
- Carlo Pinciroli | Email: cpinciroli@wpi.edu | Affiliation: Robotics Engineering Department, Worcester Polytechnic Institute
- Tianpei Yang | Email: tianpei1@ualberta.ca | Affiliation: Computer Science Department, University of Alberta
Workshop Website
More information and submission details can be found on our website:
https://www.is3rlab.org/aaai25-cmasdl-workshop.github.io
W12: Deployable AI Workshop
Artificial Intelligence (AI) has rapidly evolved into a multifaceted research domain, with recent generative models like Gemini, GPT-4, Claude, and Llama demonstrating remarkable capabilities across diverse tasks. While their potential is immense, real-world deployment requires addressing not only important technical challenges but also ethical and societal ones.
This workshop will address these critical research questions for responsible AI deployment. Specifically, the workshop will focus on algorithmic, systemic, and societal considerations to ensure that our AI models adhere to rigorous standards for fairness, ethics, explainability, privacy, and security. This workshop highlights these interdisciplinary and interrelated considerations that impact the real-world deployability of an AI model, and its responsible usage in a societally useful manner.
The 3rd Workshop of Deployable AI (DAI 2025) will be held at the AAAI 2025 conference on March 3rd/4th, 2025, with a special focus on the deployability aspects of large language models (LLMs).
Topics
We invite submissions on the theoretical foundations, algorithmic techniques, and practical strategies to ensure the responsible deployment of AI models. The topics of interest include, but are not limited to, the following:
- Deployable AI: Concepts and Models
- Privacy-Preserving AI
- Language Models & Deployability
- Explainable and Interpretable AI
- Fairness and Ethics in AI
- AI models and social impact
- Trustworthy AI models
Format
Length of the workshop: One day
Invited Talks: Talks by eminent researchers in the field.
Contributed Talks: Short talks based on the accepted papers.
Poster session: Poster presentation of all accepted papers
Submission Instructions
You are invited to submit:
– Poster/short/position papers (up to 4 pages)
– Full papers (up to 7 pages)
The submissions should adhere to the AAAI paper guidelines available at https://aaai.org/conference/aaai/aaai-25/submission-instructions/
Submissions Link: https://easychair.org/conferences/?conf=dai2025
Important Dates
Workshop Submissions Due to Organizers: November 22, 2024
Notifications Sent to Authors: December 9, 2024
Workshop to be held on March 3rd/4th, 2024
Organizers
- Balaraman Ravindran(Senior member AAAI) Chair and main contact, ravi@dsai.iitm.ac.in, RBCDSAI, IIT Madras
- Danish Pruthi, danishp@iisc.ac.in, Computational and Data Sciences, IISC Bangalore.
- Aditi Raghunathan, raditi@cmu.edu, Computer Science Department, CMU
- Krishna Pillutla, krishnap@dsai.iitm.ac.in, School of DSAI IIT Madras
- Arpita Biswas arpita.biswas@live.in, Department of Computer Science, Rutgers University
- Aravindan Raghuveer araghuveer@google.com, Google Deepmind India.
Working Committee
- Arun Rajkumar, arunr@dsai.iitm.ac.in, School of DSAI, IIT Madras.
- Harish Guruprasad, hariguru@dsai.iitm.ac.in, School of DSAI, IIT Madras.
- Chandrashekar Lakshminarayanan, chandrashekar@dsai.iitm.ac.in, School of DSAI, IIT Madras
- Preksha Nema, preksh@google.com, Google Research India
- Gokul S Krishnan, gokul@cerai.in, CeRAI, IIT Madras.
- Devika Jay, devikajay@gmail.com, CeRAI, IIT Madras.
- Rahul Vashisht rahul@cse.iitm.ac.in, PhD scholar, CSE, IIT Madras
Workshop URL
W13: Economics of Modern ML: Markets, Incentives, and Generative AI
Generative AI is starting to have have deep and profound im- pacts on multi-agent strategic settings. Our workshop will be concentrated on this research direction, covering emergent research topics such as mechanism design with/for gener- ative AI, using large language models in strategic settings as well as using them as proxies of human economic/social agents in empirical studies.
The main objective of this workshop is to push this re- search direction forward by: (1) hosting a collection of talks on this topic. (2) enabling researchers to submit and publish workshop papers, present them in poster sessions, and re- ceive feedback on them. (4) enabling researchers to discuss and exchange ideas on this topic.
Format of the Workshop
The Workshop will be a one- day workshop. The workshop will include invited talks as well as spotlight and lightning talks from a subset of the accepted workshop papers. Further, we will have a poster session for accepted workshop papers. Below are the details of our proposed schedule.
9:00–9:10: Opening Remarks
9:10–9:30: Spotlight Talk
9:30–10:10: Invited Talk
10:10–10:30: Lightning Talks
10:30–11:00: Break
11:00–11:35: Invited Talk
11:35–12:10: Invited Talk
12:10–12:30: Spotlight Talk
12:30–2:00: Lunch
2:00–2:35: Invited Talk
2:35–3:30: Poster Session
3:30–4:00: Break
4:00–4:20: Spotlight Talk
4:20–4:55: Invited Talk
4:55–5:00: Closing Remarks
Attendance
Criteria for invitation and maximum number of attendees. Invited speakers will be leading members in the research community with expertise in the area. We plan on having the speakers cover a diverse set of topics. Further, high quality workshop paper submissions will be invited to give a talk.
Other participants can register for the workshop without invitation. We expect that there would be significant interest in the topic but following the AAAI guidelines we will not have more than around 60 participants.
Submission Requirements
Papers should follow the AAAI 2025 template and are restricted to be a total of 7 pages excluding references. Further, an appendix maybe included. For a list of relevant research papers, see the follow- ing: (Feizi et al. 2023; Duetting et al. 2024; Hajiaghayi et al. 2024; Lewis et al. 2020; Dubey et al. 2024; Fish et al. 2023; Mordo, Tennenholtz, and Kurland 2024; Bianchi et al. 2024; Deng et al. 2024; Jeong et al. 2023; Li et al. 2024; Fish, Gonczarowski, and Shorrer 2024; Yao et al. 2024; Esmaeili et al. 2024; Horton 2023; Park et al. 2023; Zhao et al. 2024; Laufer, Kleinberg, and Heidari 2024)
Submission Site https://openreview.net/forum?id=xHIgp3JT08
Workshop Chairs and Committee
- Seyed Esmaeili (contact person): Postodoc, University of Chicago, esmaeili@uchicago.edu.
- MohammadTaghi Hajiaghayi: Professor, University of Maryland, hajiAghayi@gmail.com.
- Renato Paes Leme: Research Scientist, Google Research NYC, renatoppl@google.com.
- Qingyun Wu: Assistant Professor, Pennsylvania State University, qingyun.wu@psu.edu.
- Haifeng Xu: Assistant Professor, University of Chicago, haifengxu@uchicago.edu.
Note: We have included all workshop organizers as workshop chairs.
Workshop Website
This is a link to the workshop website. We will polish it and populate it with all details shortly: https://markets-incentives-genai.github.io
W14: Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data)
Foundation models highly depend on the data they are trained on. Although self-supervised learning is one of their promises, it is clear that the carefully processed datasets lead to better models. While datasets and models are frequently released by the community, the data preparation recipes are relatively nascent and not fully open. In this workshop, we invite contributions and collaborations in data preparation recipes for creating and using foundation models and generative AI applications, including (but not limited to) pre-training, alignment, fine tuning, and in-context learning. Data preparation spans data acquisition, cleaning, processing, mixtures, quality assessments, value of data, ablation studies, safety, and governance. This workshop emphasizes the responsible usage and ethical considerations of data preparation (including human annotations), to address the issues of diversity, bias, transparency, and privacy.
Topics
We encourage submissions under one of these topics of interest, but we also welcome other interesting and relevant research for preparing good data.
- Data acquisition, cleaning, processing, and mixture recipes
- Data quality assessment and quantifying the value of data
- Data sequence for multi-phase and curriculum learning
- Model-based data improvement techniques
- Ablation study strategies to understand the interplay between data and model
- Data safety and governance
- Responsible and ethical considerations of data collection and human annotation
- Diversity, bias, transparency, and privacy of data
- Theoretical modeling and analysis of data-related aspects in generative AI
- Large-scale data processing (intersection between systems and algorithms)
- Data value
Papers will be peer-reviewed under a double-blind policy. Accepted papers will be presented at the poster session, some as oral presentations, and one paper will be awarded as the best paper.
Important Dates
- Workshop paper submission deadline: 20 November 2024, 11:59 pm AoE.
- Notification to authors: 12 December 2024.
- Camera-ready deadline: TBD.
Open Review Submission Link
Please submit your paper via the following link: https://openreview.net/group?id=AAAI.org/2025/Workshop/GoodData
Submission Guidelines
- We accept submissions of a maximum of 4 pages (excluding references and appendix).
- We accept only original works not published before at any archival venue with proceedings.
- The submitted manuscript should follow the AAAI 2025 paper template.
- Submissions will be rejected without review if they:
- Contain more than 4 pages (excluding references and appendix).
- Violate the double-blind policy.
- Violate the dual-submission policy for papers.
- The accepted papers will be publicly accessible on OpenReview, but the workshop is non-archival and does not have formal proceedings.
- Papers will be peer-reviewed under a double-blind policy and must be submitted online through the OpenReview submission system.
W15: Innovation and Responsibility for AI-Supported Education
The Innovation & Responsibility in AI-supported Education (iRAISE) workshop builds on last year’s success of the AI for Education: Bridging Innovation and Responsibility workshop. It will explore the opportunities and challenges of using generative AI technologies (GenAI) in education, fostering an understanding of GenAI’s role in shaping the future of education and discussing the related ethical implications of responsible AI (RAI). Recognizing GenAI’s challenges, such as content hallucination, complex reasoning, bias, and privacy concerns, the event targets an interdisciplinary discussion of these issues for the responsible implementation of these models in education. Workshop participants will represent experts and students from industry and academia. Invited talks and accepted papers will discuss topics about responsible AI across the educational ecosystem. The workshop aims to connect individuals from different professional contexts to share expertise and inspire discussions that support new research and implementations about how to build sustainable, ethical responsible AI practices for education.
Submission Requirements
We invite full papers (up to 6 pages + references) and short papers (2 pages + references; demo papers, Work-in-progress) on the following topics of interest listed below.
Topics
- Pedagogical alignment. Application of cognitive and learning science principles to enhance GenAI-based learning frameworks for effective tutoring;
- Enhancing Language Models for educational tasks. Novel or improved architectures and algorithms, reward modeling and reinforcement learning (with human and/or AI feedback), self-correction mechanisms and inclusion of educator/learner feedback, finetuning and domain adaptation for heightening subject expertise (e.g., with Small Language Models or SLMs);
- Responsible innovation. Strategies for effective integration of responsible AI (RAI) principles into GenAI-powered educational applications including privacy, fairness, and interpretability;
- Content development. Research into GenAI-powered educational content development, assessment, and tutoring systems, as well as methods and metrics for evaluating generated content;
- Personalization. Research into applications of reinforcement learning, recommendation systems, and knowledge tracing to personalized adaptive curricula and computerized adaptive tests;
- Impact evaluation. Research evaluating the impact of AI for education interventions on learning, academic, and career outcomes;
- Educator support. AI-powered support for educators such as intervention recommendation, student work evaluation, content curation, and AI/ML-driven evaluation of instructional policies;
- Academic integrity. Analyses, studies, and solutions for preserving academic integrity in the abundance of GenAI models and AI-generated educational content;
- Benchmark datasets. Datasets for GenAI applications in education (e.g., generated educational content and assessments, student AI-tutor interactions). Special emphasis should be placed on where the data originated from, who controls and accesses the data, how student data is protected, and whether the data is fair and representative.
Submission Site Information
All submissions must follow the PMLR style template. Accepted full papers will be invited to submit posters spotlights and posters at the workshop as well as an extended version, addressing the reviewer remarks, to PMLR (https://proceedings.mlr.press/) to be published as part of the Workshop Proceedings.
To ensure a fair review process, all submissions will be evaluated through a double-blind review. All submissions must be made through the OpenReview portal for the workshop (https://bit.ly/iraise-25-submit). Authors must have an OpenReview account to make submissions. Please adhere to the submission guidelines outlined on the workshop website.
Workshop URL
https://bit.ly/iraise-25; For any questions email us at iRAISE-25-aaai@googlegroups.com.
Important Dates
October 20, 2024: Submission opens
Friday, November 22, 2024: Workshop Submissions due
Monday, December 9, 2024: Notifications Sent to Authors
March 3, 2025: Workshop Program
Attendance
We expect to attract around 50 attendees and 30 submissions. We are offering a small number of travel scholarships to promote the attendance of unrepresented students/postdocs.
Workshop Organization
Debshila Basu Mallick (SafeInsights, Rice University; Main contact), Muktha Ananda (Google), Jill Burstein (Duolingo), James Sharpnack (Duolingo), Jack Wang (Adobe), Simon Woodhead (Eedi, UK)
W16: MARW: Multi-Agent AI in the Real-World Workshop
The advent of AI Agents in real-world decision making applications has made it important to ground the knowledge of AI Agents research focusing on Human-AI and AI-AI interaction. Such AI Agents can be personalized to assist humans in day-to-day tasks and can help improve planning, reasoning, navigation with AI models, especially large models to serve many use cases and are capable of taking actions in order to perform tasks aligned to humans’ goals.
The focus of this AAAI workshop is to highlight evolving AI Agents research based on many Machine Learning (ML), Game Theory (GT) and Operations Research (OR) paradigms such as:
- Multi-Agent Deep Reinforcement Learning
- Multi-Agent Imitation Learning
- Multi-Agent Meta Learning
- Multi-Agent Self-Supervised Learning
- Multi-Agent Semi-Supervised Learning
- Game Theory
- Computational Social Choice
- Multi-Task Learning
- Goal-Conditioned Learning
- Transfer Learning
- Continual Learning and Open-Ended Learning
- Curriculum Learning
- Theoretical Research on AI Agents for real world deployment
- Any other ML/GT/OR agentic paradigms
Research papers at this AAAI workshop can highlight AI Agents research in real-world applications like:
- Robotics
- Augmented Reality
- Self-Driving Cars
- Fitness
- Web Agents
- Supply Chain Orchestration
- Climate Conservation
- Recommender Systems
- E-Commerce and Advertising
- Any other Agentic Applications in the real world
We have the following tracks at the AAAI Workshop on MARW:
- TRACK 1: Short Research Paper Track of 4 pages
- TRACK 2: Long Research Paper Track of 8 pages
- TRACK 3: Special Research Paper Track for Multimodal Agents in the Real World for 4-8 pages
- TRACK 4: Survey Paper Track of 4-8 pages
The 3 research tracks cover papers on learning algorithms, evaluation, explainability, and benchmarks of collaborating AI Agents with humans or other AI agents and competing AI Agents with other AI Agents. The survey paper track is aimed at grounding knowledge in AI Agents. Accepted survey papers will be consolidated by the academic-research industry coalition of AI Agents researchers for a joint survey paper submission to JMLR. Further details and workshop track announcements will be available at https://sites.google.com/view/marw-ai-agents/home.
Workshop Chair
Saptarashmi Bandyopadhyay, marw.ai.agents.workshop@gmail.com
Organizing Committee
- Saptarashmi Bandyopadhyay, University of Maryland, College Park & Google AI AR PhD Student Researcher
- Aleksandra Faust, Research Director, Google DeepMind, Mountain View, USA
- John Dickerson, Associate Professor of Computer Science at the University of Maryland, College Park and Chief Scientist, ArthurAI
- Arundhati Banerjee, PhD Student, Machine Learning Department, Carnegie Mellon University
Submission Details
Submissions are non-archival, and can be available on non-archival submission servers such as arXiv, under concurrent submission, or concurrently published at AAAI-25 in the main track. Submissions will be peer reviewed under a single-blind system. Your work will be assessed based on its relevance to the workshop, novelty, technical contribution, significance of impact, clarity, and reproducibility.
All submissions must follow the AAAI-25 formatting guidelines and use the corresponding LaTex style files. We accept two types of submissions – full research papers no longer than 8 pages (not including references or supplementary material) and short/poster papers no longer than 4 pages (not including references or supplementary material). Submissions will be accepted via OpenReview. For any questions related to the submission process, please contact us via marw.ai.agents.workshop@gmail.com.
All accepted papers will be presented at the workshop in-person as posters and posted on this website https://sites.google.com/view/marw-ai-agents/home. At least one author of each accepted paper must attend the AAAI workshop in-person to present their work and attend the Q&A session. A Best Paper Award will be given to a paper in each track who will be provided time slots for oral presentation.
Important Dates
Submission deadline: Monday, 25 November 2024 (Anywhere on Earth)
Notification of acceptance/rejection: Thursday, 12 December 2024
Workshop: Monday, 3 March 2025
W17: Planning in The Era of Large Language Models
Large Language Models (LLMs) are a disruptive force, changing how research was done in many sub-areas of AI. Planning is one of the last bastions that remain standing. The focus of this workshop is on the questions in the intersection of these areas. Some of the specific areas we would like to gain a better understanding in include: what LLMs can contribute to planning, how LLMs can/should be used, what are the pitfalls of using LLMs, what are the guarantees that can be obtained.
Topics
- Planning directly with pre-trained or fine-tuned LLMs.
- LLMs for (partial) model elicitation.
- LLMs for search guidance or search pruning.
- Validation/verification of plans, policies, or models.
- Generalization in planning with LLMs.
- Planning for LLMs.
- Using LLMs to develop interfaces for planning-based systems.
- Using LLMs as a proxy for user preferences.
- Validation/verification of plans, policies, or models.
- Generalization in planning and generalized planning with LLMs.
- Using LLMs to develop interfaces for planning-related problems.
- Other applications of LLMs in planning.
- Other applications of large vision-language models (VLMs) in planning.
- Planning for LLMs and VLMs.
Format of Workshop
We plan for a full day, four sessions. The sessions will include:
- Four invited talks by established experts in the field
- Full paper presentations for select accepted papers
- A panel on emerging topics in planning with LLMs
- A discussion on issues and pitfalls of current trends
In parallel, we plan to have a poster session throughout the workshop day to facilitate discussions among participants.
Attendance: The workshop targets an audience interested in planning with large language models and we would like to see a broader AI community as well as the planning community.
Submission Requirements
- Full papers – 8 pages
- Short papers – 4 pages
- No limits on references or appendices
Submission Site Information: https://openreview.net/group?id=AAAI.org/2025/Workshop/LM4Plan
Workshop Chair
Michael Katz, Michael.Katz1@ibm.com
Workshop Committee
- Jiayuan Mao (jiayuanm@mit.edu)
- Wenlong Huang (wenlongh@stanford.edu)
- Sarath Sreedharan (sarath.sreedharan@colostate.edu)
- Subbarao Kambhampati(rao@asu.edu)
Workshop URL
https://llmforplanning.github.io/
W18: Post-Singularity Symbiosis: Preparing for a World with Superintelligence
The rapid advancement of AI technology increases the likelihood of superintelligence emerging soon, potentially acting beyond human control. In such a world, humanity must seek coexistence with superintelligence. The emerging field of “Post-Singularity Symbiosis” (PSS) addresses this challenge, aiming to expand human survival and welfare opportunities in a superintelligence-dominated context.
While existing AI safety approaches often assume human initiative, PSS uniquely considers scenarios where superintelligence holds the initiative. This workshop explores preventive measures against the potential insufficiency of current strategies, focusing on three key areas: analysis of superintelligence, its guidance, and enhancement of human capabilities.
By convening experts from diverse fields, we aim to foster innovative discussions, expand the PSS research community, and generate further research opportunities on human-superintelligence symbiosis. Our ultimate goal is to develop a comprehensive approach to this critical issue, contributing to humanity’s long-term prosperity and welfare in a post-singularity world.
Topics
Here, we list the main research areas of PSS
- Superintelligence analysis: Accumulating fundamental knowledge about superintelligence’s motivations, decision-making processes, and behaviors
- Superintelligence guidance: Developing practical approaches to influence superintelligence in ways desirable for humanity
- Human enhancement: Strategies for human adaptation and value redefinition to coexist with superintelligence
For more details, please see the workshop page below.
Format of Workshop
We plan to organize a one-day workshop consisting of keynote speeches, invited presentations, oral and poster paper presentations, and a panel discussion.
Attendance:
The workshop is open to all interested in post-singularity symbiosis-related topics, with an expected attendance of up to 50 people. While anyone can attend, only authors of accepted papers may present. At least one author of each accepted submission must attend the workshop to present their work in either the oral or poster session.
Submission Requirements
We welcome submissions prioritizing relevance to the workshop themes over novelty, including ongoing research and previously published work. All submissions must adhere to the AAAI-25 format ( https://aaai.org/conference/aaai/aaai-25/submission-instructions/ ).
Three types of submissions are accepted:
- full research papers, which should not exceed 8 pages (including references)
- short/poster papers, which should not exceed 4 pages (including references)
- extended abstracts, which should not exceed 2 pages.
The review process will be single-blind. Please submit your paper as a PDF via our portal. We encourage authors to post their submissions to preprint servers to promote early dissemination of ideas.
Submission Site Information: https://openreview.net/group?id=AAAI.org/2025/Workshop/PSS
Workshop Chair
Hiroshi Yamakawa, pss-2025@aialign.net
Workshop Committee
- Hiroshi Yamakawa (The University of Tokyo / AI alignment network), pss-2025@aialign.net
- Yusuke Hayashi (AI alignment network), pss-2025@aialign.net
- Yoshinori Okamoto (YUASA AND HARA), pss-2025@aialign.net
- Masayuki Nagai ( Cold Spring Harbor Laboratory), pss-2025@aialign.net
- Ryota Takatsuki (The University of Tokyo), pss-2025@aialign.net
- Satoshi Kurihara (Keio University), pss-2025@aialign.net
- Kenji Doya (The Okinawa Institute of Science and Technology), pss-2025@aialign.net
Workshop URL
W19: Preventing and Detecting LLM Generated Misinformation
As large language models (LLMs) become more sophisticated and pervasive, the risk of misinformation they generate poses significant challenges. This workshop aims to address the specific issues related to misinformation produced by LLMs, focusing on both prevention and detection strategies.
The widespread use of LLMs makes addressing misinformation they generate more urgent than ever. As these models become more advanced, they can produce text that seems credible but may contain false information, impacting areas like healthcare, finance, and public policy.
This workshop will bring together researchers and practitioners to foster collaboration, share insights, and inspire new research directions in the responsible development and deployment of LLM technologies. By focusing on these key issues, we aim to mitigate the harm caused by LLM-generated misinformation across various domains.
Topics of Interest:
- We invite papers that focus on various aspects of mitigating misinformation generated by LLMs. Topics of interest include (but are not limited to):
- Hallucination detection and mitigation in LLMs
- Alignment techniques for LLMs
- Editing and improving LLM knowledge for enhanced factuality
- Watermarking techniques for identifying LLM-generated text
- AI-generated text detection methods
- Fake news detection using LLMs
- Evaluating the impact of LLM-generated misinformation on society
- Developing responsible deployment strategies for LLMs
- Ethical considerations in mitigating LLM-generated misinformation
- Collaborative human-AI approaches to combat misinformation
- Developing benchmarks and evaluation metrics for LLM-generated misinformation
- Integrating fact-checking and verification methods with LLMs
- Multimodal misinformation detection and mitigation
- Cross-lingual and multilingual misinformation challenges and solutions
Important Dates:
Submission deadline: December 10, 2024
Notification to authors: January 5, 2025
Camera-ready deadline: January 20, 2025
Workshop date: March 3, 2025 (Afternoon)
Submission Guidelines:
We welcome two types of submissions for this workshop:
- Full papers (up to 8 pages)
- Short papers (up to 4 pages)
All submissions must be in English and follow the AAAI conference proceedings format. The page limits include all content except references. Submissions must be original work that has not been previously published at other conferences. Simultaneous submission to other conferences or workshops is permitted.
Submission Process:
- All papers should be submitted through the workshop’s Openreview submission system [https://openreview.net/group?id=AAAI.org/2025/Workshop/PDLM].
- Papers must be submitted in PDF format.
- Authors should ensure that their submissions are anonymous for double-blind peer review.
Formatting Requirements:
- Use the latest AAAI conference proceedings template, available at: https://aaai.org/conference/aaai/aaai-25/submission-instructions/
- Include an abstract of no more than 200 words.
Review Process:
All submissions will undergo a double-blind peer review process. Each paper will be reviewed by at least two members of the program committee based on its originality, relevance to the workshop, technical soundness, and clarity of presentation.
Presentation:
Authors of accepted papers will be invited to present their work at the workshop. Presentation details will be provided upon acceptance.
Workshop Organizers:
- Xuming Hu, Hong Kong University of Science and Technology (Guangzhou)
- Jing Ma, Hong Kong Baptist University
- Kai Shu, Emory University
- Hui Xiong, Hong Kong University of Science and Technology (Guangzhou)
- Philip S. Yu, University of Illinois at Chicago
- Aiwei Liu, Tsinghua University
Contact Information:
For any questions regarding submissions or the workshop in general, please contact the workshop organizers at xuminghu@hkust-gz.edu.cn
We look forward to your submissions and participation in this important workshop on preventing and detecting LLM generated misinformation.
Website URL:
W20: Privacy-Preserving Artificial Intelligence
The rise of machine learning, optimization, and Large Language Models (LLMs) has created new paradigms for computing, but it has also ushered in complex privacy challenges. The intersection of AI and privacy is not merely a technical dilemma but a societal concern that demands careful considerations.
In its sixth edition, the AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-25) will provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI.
The emphasis will be placed on: Policy considerations and legal frameworks for privacy; Broader implications of privacy in LLMs; and The societal impact of privacy within AI.
Topics
We invite three categories of contributions: technical (research) papers, position papers, and systems descriptions on these subjects:
- Differential privacy: Applications
- Differential privacy: Theory
- Contextual integrity
- Attacks on data confidentiality
- Privacy and Fairness interplay
- Legal frameworks and privacy policies
- Privacy-centric machine learning and optimization
- Benchmarking: test cases and standards
- Ethical considerations of LLMs on users’ privacy
- The impact of LLMs on personal privacy in various applications like chatbots, recommendation systems, etc.
- Case studies on real-world privacy challenges and solutions in deploying LLMs
- Privacy-aware evaluation metrics and benchmarks specifically for LLMs
- Interdisciplinary perspectives on AI applications, including sociological and economic views on privacy
- Evaluating models to audit and/or minimize data leakages
Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.
Important Dates
- November 24, 2024 – Submission Deadline
- December 15, 2024 – Acceptance Notification
- March 3, 2025 – Workshop Date
Format
The workshop will be a one-day meeting. The workshop will include a number of technical sessions, a poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a number of tutorial talks, and will conclude with a panel discussion.
Attendance
Attendance is open to all. At least one author of each accepted submission must be present at the workshop.
Submission
Submission URL: https://cmt3.research.microsoft.com/PPAI2025
- Technical Papers: Full-length research papers of up to 9 pages (excluding references and appendices) detailing high quality work in progress or work that could potentially be published at a major conference.
- Extended Abstracts: Position or brief description of initial work (1 page, excluding references and appendices) or the release of privacy-preserving benchmarks and datasets on the topics of interest.
NeurIPS/AAAI Fast Track (Rejected AAAI papers)
Rejected NeurIPS/AAAI papers with average scores of at least 4.5 may be submitted directly to PPAI along with previous reviews. These submissions may go through a light review process or accepted if the provided reviews are judged to meet the workshop standard.
Submissions are accepted in two separate tracks:
– Technical Privacy Track
– Policy Privacy Track
All papers must be submitted in PDF or Word format, using one of the following templates. LaTeX Template, Word Template
Submissions should be anonymized and thus NOT include the name(s), affiliations, or email addresses of any author. Submissions will be refereed on the basis of technical quality, novelty, significance, and clarity. Each submission will be thoroughly reviewed by at least two program committee members.
NeurIPS/AAAI fast track papers are subject to the same page limits of standard submissions. Fast track papers should be accompanied by their reviews, submitted as a supplemental material.
For questions about the submission process, contact the workshop chairs.
Workshop Chair
Ferdinando Fioretto [fioretto@virginia.edu] (University of Virginia)
Workshop Committee
- Ferdinando Fioretto (University of Virginia) – fioretto@virginia.edu
- Juba Ziani (Georgia Institute of Technology) – jziani3@gatech.edu
- Wanrong Zhang (TikTok & University of British Columbia) – imwanrongz@gmail.com
- Jeremy Seeman (Urban Institute) – JSeeman@urban.org
Workshop URL
W21: Quantum Computing and Artificial Intelligence (QC+AI)
Quantum computers, albeit on a small scale, are becoming more accessible to the public, e.g., through IBM, Google, and D-Wave. Naturally, this calls for exploiting quantum computers to enhance classical Artificial Intelligence (AI), e.g., to improve their prediction performance or enable faster training by exploiting quantum mechanical principles such as superposition and entanglement. To this end, there is a growing interest in quantum artificial intelligence (QAI) to exploit quantum computing (QC) to enhance classical AI techniques. This workshop focuses on seeking contributions encompassing theoretical and applied advances in QAI.
On the other hand, there is also an increasing interest in the application of classical AI techniques for solving problems within QC (AI4QC), such as in quantum software engineering, quantum circuit design, and optimizing quantum optimization approaches (e.g., minor embedding in quantum annealing). Consequently, we also seek contributions that apply classical AI techniques in various aspects of QC.
Many AI problems can be cast as optimization problems, and we also welcome contributions formulating AI problems as optimization tasks, e.g., Quadratic Unconstrained Binary Optimization (QUBO) to be solved by quantum annealers.
Topics
- Theoretical foundations of quantum AI algorithms
- Quantum AI applications in any domain, e.g., transportation, chemistry, simulations, physics, etc.
- Classical AI techniques in the area of quantum circuit design, such as optimizing quantum circuit compilation and transpilation
- Quantum noise reduction with classical AI techniques
- Classical AI techniques for quantum software engineering, including quantum software testing, debugging, and repair
- Applications of large-language models for quantum circuit design and quantum software engineering
- Quantum AI techniques for quantum software engineering
- Classical AI techniques for optimizing quantum search and optimization algorithms such as QAOA
- Quantum annealing and its applications
- QUBO models of AI problems
- Novel quantum machine learning algorithms such as theory and applications of quantum reservoir computing and quantum extreme learning machines
Format of Workshop
It will be a one-day workshop. We plan to have an invited keynote speaker, paper presentations, and a panel discussion. We may invite additional speakers if the number of paper submissions is low.
Attendance
There is no special criteria.
Submission Requirements
Please follow AAAI formatting instructions.
https://aaai.org/conference/aaai/aaai-25/submission-instructions/
- Full Paper: 8 Pages
- Work in Progress: 4 pages maximum
- Lightning Talks: Only abstract
Submission Site Information:
https://easychair.org/my/conference?conf=qcai2025
Workshop Chair
Shaukat Ali, shaukat@simula.no
Workshop Committee
- Shaukat Ali, Simula Research Laboratory Norway, shaukat@simula.no
- Francisco Chicano, University of Malaga Spain, chicano@uma.es
- Alberto Moraglio, University of Exeter UK, A.Moraglio@exeter.ac.uk
Workshop URL
W22: Web Agent Revolution: Enhancing Trust and Enterprise-Grade Adoption Through Innovation
The Web Agent Revolution workshop at AAAI 2025 focuses on advancing the development of general web agents through innovative benchmarks, datasets, and agent architectures. Web agents—autonomous AI systems capable of navigating and interacting with the web—have seen rapid technological advancements, however existing agents lack essential components to ensure safeguards mandates for enterprise adoption, and evaluation benchmarks lack rigorous methods for testing those safeguards. This workshop addresses key challenges in improving trustworthiness and reliability in real-world settings, making it a critical discussion for academia and industry.
Topics:
- Standardizing and open source benchmark development (e.g., BrowserGym)
- Novel methods for scaling realistic benchmarks
- Generating benchmarks from multi-modal human demonstrations
- Multi-agent architectures, design principles and implementations for improving accuracy, safety, and trustworthiness
- Multi-Modal techniques for observation and action space
- Agentic workflows in enterprise contexts
- New metrics and evaluation functions
Format of Workshop
This 1-day workshop will feature invited talks from experts at leading companies an industry-academia panel, spotlight research presentations, and interactive discussions. The workshop aims to foster collaboration and practical insights into improving web agent technologies, focusing on agentic workflows, scalability, and reliability for enterprise applications.
Attendance
The workshop is open to researchers, practitioners, and industry professionals interested in web agents, AI, and automation. There are no specific criteria or maximum number of attendees.
Submission Requirements
We invite submissions of extended abstracts (up to three pages) presenting the latest and greatest research, case studies, or position papers relevant to the workshop topics. Submissions should follow the AAAI formatting guidelines.
Submission Site Information
Please submit your papers via https://easychair.org/conferences/?conf=waretea1.
Workshop Chairs:
- Segev Shlomov (IBM Research, segev.shlomov1@ibm.com)
- Xiang Deng (Google, xiangdeng@google.com)
- Ronen Brafman (Ben-Gurion University, brafman@bgu.ac.il)
- Avi Yaeli (IBM Research, aviy@il.ibm.com)
Workshop Committee: Members from academic and industry partners will be listed on the workshop’s website.
Workshop URL
For more information, please visit https://sites.google.com/view/web-agent-revolution/home.
Join us at AAAI 2025 to drive the future of web agents, ensuring their readiness for enterprise-scale deployment.
Description
Imageomics is an emerging interdisciplinary scientific field focused on understanding biology of organisms, particularly the biological traits and observable phenotype, from visual data, ranging from microscopic cell images to videos of charismatic megafauna. A central goal of Imageomics is to make traits computable from images by grounding AI models in existing scientific knowledge. The goal of this workshop is to nurture the community of researchers working at the intersection of AI and biology and shape the vision of the nascent yet rapidly growing field of Imageomics.
Topics
We encourage participation from researchers in a broad range of topics exploring AI/ML techniques to understand characteristic patterns of and underlying processes governing organisms from visual data. Research questions may include (but are not limited to): What are the types and characteristics of knowledge and data in biology that can be integrated into AI methodologies, and what are the mechanisms for this integration? How best can new knowledge exposed by ML be translated back into the knowledge corpus of biology? How best can we inform and catalyze a community of practice to utilize and build upon Imageomics to address grand scientific and societal challenges? How can we leverage or construct foundation models to benefit biological and ecological insight?
Format of Workshop
Our 1-day workshop will include keynote/invited talks, contributed paper presentations, poster sessions, and a panel discussion.
Attendance
We welcome participation from anyone interested in learning about the field of Imageomics, including biologists working on problems with image data and biological knowledge available such as phylogenies, taxonomic groupings, ontologies, or evolutionary models, and AI researchers working on topics such as explainability, generalizability, open world recognition, foundation and generative models, who are looking for novel interdisciplinary research problems.
Submission Requirements
We are accepting short paper submissions for position, review, or research results (2-4 pages, excluding references). Shorter versions (6 pages, excluding references) of articles accepted at other venues are acceptable provided it does not violate their dual-submission policy. All submissions will undergo peer review and authors may publish in arXiv proceedings.
Submission Site Information https://imageomics.osu.edu/aaai25
Organizing Committee
- Wei-Lun Chao (The Ohio State University, chao.209@osu.edu, primary contact)
- Anuj Karpatne (Virginia Tech, karpatne@vt.edu)
- Jianyang Gu (The Ohio State University, gu.1220@osu.edu)
- Yu Su (The Ohio State University, su.809@osu.edu)
- Charles Stewart (Rensselaer Polytechnic Institute, stewart@rpi.edu)
- Tilo Burghardt (University of Bristol, tilo@cs.bris.ac.uk)
- Tanya Berger-Wolf (The Ohio State University, berger-wolf.1@osu.edu)
Workshop URL
https://imageomics.osu.edu/aaai25
Funding Acknowledgement
This workshop is supported by the National Science Foundation under Award No. 2118240 “HDR Institute: Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning.”
W24: Workshop on Datasets and Evaluators of AI Safety
Advanced AI systems have the potential to drive economic growth and productivity, boost health and well-being, improve public services, and increase security. However, AI models can also cause societal harms and can be misused. This workshop focuses on evaluating the safety of AI models and in particular on LLMs. We are especially interested in work on improving datasets and benchmarks, as well as devising methods for evaluating the safety of AI models through the development of evaluators.
The goals of this full-day workshop, organised in collaboration with Kaggle, King’s College London and the Open Data Institute are to bring together academic and industrial researchers working on datasets and evaluators for AI safety.
Concerns regarding the safety of AI emerge from the potential harmful uses or consequences of AI outputs, which can result from inaccuracies, irresponsibility, or inappropriate applications. As AI becomes increasingly integrated into critical systems and everyday activities, addressing AI safety issues is imperative. The misuse of AI technologies for generating misinformation, conducting sophisticated cyberattacks, developing weapons, or providing harmful advice presents grave concerns.
AI can cause societal harm, encouraging radicalisation and promoting biased or skewed views. AI-generated fake, yet highly realistic content could reduce public trust in information and government bodies. Moreover, long-term existential risks associated with the development of superintelligent AI systems cannot be ignored.
A significant portion of these safety concerns can be attributed to data-related problems at various stages of the AI lifecycle. The growing adoption of frontier foundation models in mainstream applications has amplified these concerns. Specifically, the lack of transparency regarding the data used to pre-train these models and the data approaches to fine-tuning these models for custom applications can lead to unintended consequences.
Characteristics of AI systems that need to be evaluated to ensure their safety include, but are not limited to, alignment, robustness to adversarial attacks, fairness, trustworthiness, deception capabilities, AI drift, explainability, privacy preservation, and reliability. Evaluation of these characteristics is challenging, not less due to the lack of benchmarks that are able to certify the level of safety of a given AI system.
This workshop will explore the role of data in AI safety, with a particular emphasis on data-centric AI approaches and their current limitations across the AI lifecycle.
Topics
This workshop welcomes contributions towards creation and improvement of datasets and benchmarks used for evaluation of safety of AI systems (in particular LLMs) according to the characteristics listed above. The work can be theoretical (new approaches to creating datasets or proving a dataset’s adequacy for the task) or practical (suggesting new datasets, checking the validity of existing datasets, improving existing datasets). We also welcome contributions that use existing benchmarks to demonstrate safety or AI systems or find unsafe behaviors.
Format of Workshop
The workshop’s length is one full day.
The workshop will be a combination of invited talks, contributed talks, a panel, the winners and the runner-ups of the Kaggle challenge on AI Safety, presentations of submitted work, and poster presentations.
We will leave ample time for questions and answers at the end of each invited talk and panel session, and we will feature a poster session to encourage dialogue among authors.
The Kaggle challenge overview will be given by D. Sculley, the CEO of Kaggle and one of the workshop’s co-chairs.
Attendance
The presenters of accepted papers and posters are expected to attend in person, except in special circumstances.
The invited speakers will attend in person.
The workshop welcomes participants that do not present a paper or a poster as well.
We expect around 100 participants in person. We will make arrangements for remote attendees.
Submission Requirements
- Paper submissions are up to 4 pages in the AAAI format.
- Poster submissions – a poster and a two-pages abstract in AAAI format.
- The submission is anonymised.
Both paper and poster submissions will be peer-reviewed. Papers can be accepted for presentation as a talk and a poster or only as a poster.
There are no formal proceedings. Accepted papers and posters will be published on the workshop’s website, if the authors agree to the release of their manuscripts.
Submission Site Information
https://openreview.net/group?id=AAAI.org/2025/Workshop/DATASAFE
Workshop Chair
Hana Chockler hana.chockler@kcl.ac.uk
Frederik Mallmann-Trenn frederik.mallmann-trenn@kcl.ac.uk
Workshop Committee
D. Sculley, Kaggle, [email withheld]
Lilith Bat-Leah, Mod Op, lilith@mlcommons.org
Hana Chockler, King’s College London and the Open Data Institute, hana.chockler@kcl.ac.uk
Frederik Mallmann-Trenn, King’s College London and the Open Data Institute, frederik.mallmann-trenn@kcl.ac.uk
Workshop Website
W25: Workshop on Document Understanding and Intelligence
The rapid expansion of scientific publications and visually rich document collections poses unique challenges for researchers and practitioners across various fields. Staying up-to-date with the latest findings and identifying emerging challenges is increasingly difficult, making the development of advanced technologies to streamline document understanding essential. The Workshop on Document Understanding and Intelligence: From Textual Content to Visually-Rich Structure (W25) aims to provide a unique forum for researchers to exchange ideas and to explore cutting-edge methodologies and resources that enable a comprehensive understanding of scholarly and visually structured documents. This workshop unites the research community from diverse disciplines to discuss state-of-the-art technologies and their impact on diverse fields, from scientific research to business, law, and medicine.
Building on the foundations of last year’s Scientific Document Understanding (SDU) workshop, the 2025 workshop broadens its scope to incorporate Visually Rich Document (VRD) understanding. The morning session will focus on scientific document processing, information extraction, question answering, summarisation, and domain-specific applications of large language models (LLMs) and generative AI systems. The afternoon session will explore VRD understanding, with topics covering document structure comprehension, layout parsing, and semantic extraction from complex reports and forms. Through engaging research presentations, invited talks, and a panel discussion, this workshop aims to bridge the gap between textual and visual document processing, fostering interdisciplinary collaborations.
Topics of Interest
The workshop invites original research contributions on all document understanding and intelligence aspects. Topics of interest include, but are not limited to:
Scientific Document Understanding (SDU)
- Information extraction and retrieval for scientific literature
- Question answering and generation for scholarly texts
- Disambiguation, acronym identification, and definition extraction
- Developing LLMs and generative AI models tailored for scientific domains
- Instruction tuning, in-context learning, and other adaptive strategies for scientific documents
- Document summarization, topic classification, and machine reading comprehension
- Multi-modal and multi-lingual scholarly text processing
- Knowledge graph construction, representation, and reasoning for scholarly resources
- Survey papers on SDU advancements and unsolved challenges in different scientific domains
Visually Rich Document Understanding (VRD)
- Semantic extraction and structural parsing for visually rich documents
- Table and form understanding, document layout analysis, and diagram comprehension
- Multi-modal integration of textual, visual, and tabular data
- VRD applications in business, legal, and medical documents
- VRD challenges in handling diverse layouts and domain-specific formats
- New methodologies and benchmarks for VRD tasks in real-world scenarios
Cross-Domain and Interdisciplinary Topics
- Leveraging large language models and generative AI for both textual and visual document processing
- AI-based frameworks for document-level analysis and representation learning
- Data integration and knowledge management in hybrid text and visual document systems
- Factuality, data verification, and anti-science detection in complex document contexts
- Resource and tool development, including new datasets, models, and evaluation frameworks for SDU and VRD tasks
Workshop Format
The workshop will be a one-day event, with an expected participation of approximately 50-60 attendees. It will commence with an opening remark, followed by research paper presentations focusing on SDU in the morning session. The afternoon session will spotlight recent developments in VRD, featuring a research track as well as a leaderboard track dedicated to structural understanding in industrial reports. The workshop will conclude with a panel discussion, bringing together researchers from academia and industry to identify future directions and research gaps in document understanding.
We are excited to include a leaderboard track for the VRD tasks this year, offering participants a chance to showcase their methods. More detailed information about the competition can be found on the workshop website.
Submission Guidelines
We welcome submissions of unpublished, original research that presents novel findings and perspectives. Submissions should be in English and follow the AAAI style template. Authors may also submit supplementary materials, including technical appendices, source codes, datasets, or multimedia appendices. All submissions will undergo double-blind peer review, and the accepted papers will be presented as oral or poster presentations at the workshop. At least one author of each accepted paper must register and attend the workshop to present their work.
We encourage two types of submissions:
- Long Technical Papers: Recommended length of up to 8 pages + references.
- Short Papers: Recommended length between 3 and 5 pages + references.
Submissions should be made electronically in PDF format via the Microsoft CMT system. The submission link, deadline and other important dates will be announced on the workshop’s official webpage.
Workshop Organisers
Morning Session (Scientific Document Understanding)
- Mihir Parmar, Arizona State University, USA
- Thien Huu Nguyen, University of Oregon, USA
- Chien Van Nguyen, University of Oregon, USA
- Ryan A. Rossi, Adobe Research, USA
- Franck Dernoncourt, Adobe Research, USA
Afternoon Session (Visually-Rich Document Understanding)
- Caren Han, The University of Melbourne, Australia
- Yihao Ding, The University of Melbourne, Australia
- Josiah Poon, The University of Sydney, Australia
- Anita de Waard, Elsevier, Netherlands
- Eduard Hovy, The University of Melbourne, Australia
Website: https://sites.google.com/view/docui-aaai25/
Submission Site: https://cmt3.research.microsoft.com/DOCUIAAAI2025/Submission/Index
W26: Workshop on Multi-Agent Path Finding
Multi-Agent Path Finding (MAPF) involves computing collision-free paths for multiple agents from their starting locations to given destinations in a known environment. This problem finds diverse applications, from robot coordination to traffic management. Researchers in artificial intelligence, robotics, and theoretical computer science have been actively exploring various MAPF problem variants and solution approaches. This workshop aims to bring these researchers together to present their research, discuss future research directions, and cross-fertilize the different communities.
Topics
All works that relate to collision-free path planning or navigation for multiple agents are welcome, including but not limited to:
- Search-, rule-, reduction-, reactive-, and learning-based MAPF planners;
- MAPF solvers for non-grid non-point agents (e.g. continuous space, robot arms, etc);
- MAPF methods for execution monitoring, replanning, robustness to delays, etc;
- Combination of MAPF and task allocation, scheduling, etc.;
- Real-world applications of MAPF planners;
- Multi-agent machine learning for centralized and decentralized MAPF;
- Customization of MAPF planners for actual robots (e.g. motion and communication constraints, environment changes, etc.);
- Standardization of MAPF terminology and benchmarks.
Format of Workshop
The workshop is a One-Day workshop including invited talks, paper presentations, Q&As, community discussion, and a newly introduced Industry Panel Session, which aims to foster stronger connections and bridge the gap between industry and the MAPF research community.
Attendance
The workshop expects to invite 30 participants, including program committees, accepted authors, invited speakers, and researchers who are active in the MAPF community. People who are not invited but are interested in MAPF are welcome to attend.
Submission Requirements
Submissions can contain relevant work in all possible stages, including work that was recently published, is under submission elsewhere, was only recently finished, or is still ongoing. Authors of papers published or under submission elsewhere are encouraged to submit the original papers or short versions (including abstracts) to educate other researchers about their work, as long as resubmissions are clearly labelled to avoid copyright violations. Position papers and surveys are also welcome. Submissions will go through a light review process to ensure a fit with the topic and acceptable quality. Non-archival workshop notes will be produced containing the material presented at the workshop.
Format
Any format is acceptable.
Page limitation: There is no limit on the number of pages.
Submission Site
https://cmt3.research.microsoft.com/WoMAPF2025/Submission/Index
Important Dates
Note: all deadlines are “anywhere on earth” (UTC-12)
Paper submission deadline: November 24, 2024
Paper notification: December 9, 2024
Final version: Jan 9, 2025
Workshop: March 3 , 2025
Workshop Committee
Zhe Chen, Monash University (zhe.chen [at] monash.edu)
Jingkai Chen, Symbotic (jichen [at] symbotic.com)
Keisuke Okumura, University of Cambirdge / AIST (ko393 [at] cam.ac.uk)
Rishi Veerapaneni, Carnegie Mellon University (vrishi [at] cmu.edu)
Yue Zhang, Monash University (yue.zhang [at] monash.edu)
Advisory Board
Jiaoyang Li, Carnegie Mellon University
Daniel Harabor, Monash University
Peter Stuckey, Monash University
Sven Koenig, University of California, Irvine
Workshop URL
W27:Foundation Models for Biological Discoveries (FMs4Bio)
Foundation models (FMs) have transformed natural language understanding and computer vision. In particular, research on LLMs and multi-modal LLMs in these two domains is progressing rapidly, and this progress is starting to permeate a broad range of scientific disciplines. In this second offering of our workshop, our focus is on FMs for advancing biological discoveries. Current efforts have revealed that indeed FMs are advancing our ability to conduct biological research in silico, formulate interesting hypotheses and even design novel molecules, but biology remains complex and is ultimately a multi- systems discipline. Biology occurs when molecules come together, governed by an underlying physics advancing processes that occur at disparate spatio-temporal scales, only probed in the wet laboratories at different conditions, at different granularities, at different levels of fidelity, and incompletely. This workshop poses and advances the following question: How can we advance FMs to transform biological research? This workshop brings together an interdisciplinary community of researchers at various levels of their career to nucleate a community that advances this question.
Topics
In addition to the following research themes, we encourage novel contributions from researchers that bring different perspectives on the core focus of the workshop:
- Learning from Incomplete Data of Different Modalities
- Grounding Foundation Models in Knowledge Beyond the Data
- Reconciling Disparate Spatio-temporal scale and Varying Fidelity in Multimodal Data
- Beyond Prediction: Answering the How and the Why
- Quantifying Confidence of Predictions with Foundation Models
Format of Workshop
This is a full-day workshop.
It will be structured into sessions aligned with distinct research themes. Each session will open with a featured invited talk, with the rest focusing on presentations by authors of accepted papers. These will vary in length depending on the submission type and reviewer feedback. A final session will contain a panel discussion by senior and up-and-coming researchers, focusing on next steps for the community.
Attendance
Invited speakers and other attendees will fall into three groups:
- Foundational AI researchers
- Biological researchers that have started to utilize FMs
- Biological researchers with a track record in ML but not FMs
Based on our first successful offering at AAAI 2024, which focused on LLMs, we expect to attract at least 75 attendees. We do not expect to exceed 100 attendees.
Submission Requirements
To reflect the disciplinary diversity, we will encourage submissions of varying length:
- 1-page position papers
- 4-page papers on breaking results, datasets, benchmarks
- 6-8-page papers on more detailed investigations
Submission Site Information
Authors will submit at: https://easychair.org/my/conference?conf=fms4bio25
We have set up llms4science@gmail.com for author submission enquiries.
Accepted papers will be published at https://github.com/LLMs4Science-Community.
Workshop Chair
Amarda Shehu amarda@gmu.edu
Workshop Committee
Amarda Shehu, George Mason University, amarda@gmu.edu
Yana Bromberg, Emory University, yana@bromberglab.org
Liang Zhao, Emory University, liang.zhao@emory.edu
Workshop URL
W28: Advancing LLM-Based Multi-Agent Collaboration
This full-day workshop seeks to ignite discussion on cutting-edge research areas and challenges associated with multi-agent collaboration driven by large language models (LLMs). As LLMs continue to showcase the ability to coordinate multiple AI agents for complex problem-solving, the workshop will delve into pivotal open research questions that advance the understanding and potential of LLM-based multi-agent collaboration.
Topics
We invite submissions on a range of topics, including but not limited to:
- Architectures for multi-agent collaboration, hierarchy, and decision-making
- Cross-agent knowledge sharing
- Inter-agent communication protocols
- Distributed and decentralized agent
- Multi-agent group behavior learning
- Strategic planning in multi-agent problem-solving
- Guardrails and ethical considerations in multi-agent systems
Submission Guidelines
We welcome both short papers (up to 4 pages) and long papers (up to 8 pages) following the AAAI format. Submissions may include recently published work, under-review papers, work in progress, and position papers. All submissions will undergo peer review through a single-blind process. While workshop publication is non-archival, accepted papers will be featured on our website with author permission.
Submission Site
Please submit your work via: https://openreview.net/group?id=AAAI.org/2025/Workshop/WMAC
Attendance
The workshop is open to all registered participants. In the event of high demand, priority will be given to authors of accepted papers.
Workshop Format
The one-day workshop features invited talks, oral presentations, lightning talks, poster sessions, and a panel discussion. Additional details on speakers and the schedule will be available on our website.
Organizing Committee
- Alborz Geramifard (Meta, alborz.geramifard@gmail.com)
- Alex Marin (Microsoft, alemari@microsoft.com)
- Raphael Shu (Amazon AWS GenAI, zhongzhu@amazon.com)
- Tao Yu (The University of Hong Kong, tyu@cs.hku.hk)
- Weiyan Shi (Northeastern University, wyshiwyshi@gmail.com)
- Yi Zhang (Amazon AWS GenAI, yizhngn@amazon.com)
More Information
- Workshop website: multiagents.org/workshop
- Contact for inquires: pc@multiagents.org
W29: AI Agent for Information Retrieval: Generating and Ranking
The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with tool-call, advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop is motivated by the need to explore these challenges, share innovative solutions, and discuss future directions.
Topics Include but not limited to:
- Agentic Retrieval System
- Agentic Conversational Recommendation
- AI Agent for Personalization
- AI Agent for Sequential Recommendation
- AI Agent for Contextual Information Retrieval
- AI Agents for Cross-lingual and Multimodal Retrieval
- Retrieval-Augmented Generation Searching System
- Optimization of AI Agent Retrieval Models
- Bias Mitigation in AI-driven Information Retrieval
- Interpretable Generative Retrieval System
- Adversarial Attacks and Defenses in Agentic Retrieval System
- Ethical Considerations in Agentic Information Retrieval
- Human-AI Collaboration in Information Retrieval
- Evaluation for Agentic Information Retrieval
- Scalability and Efficiency in AI Agent Retrieval
Important Dates (AOE)
- December 15, 2024: Paper submission deadline
- January 5, 2025: Paper acceptance notification
- January 28, 2025: Paper Camera-Ready
- March 4, 2025: Workshop date
Guidelines
We mainly follow the CIKM guidelines for paper submission except for a single-blind peer review. All important guidelines are as follows:
- Single-blind peer review. There is no requirements for anonymous submission unless authors prefer.
- Full papers cannot exceed 9 pages, including an appendix, plus unlimited references (paper content is limited to 9 pages, that means that if you have an appendix, then it should be included within that page limit. It is also ok if you do not have an appendix and instead 9 pages of content). Short papers cannot exceed 4 pages plus unlimited references.
- AAAI two-column format is often preferred. The AAAI 2024 Author Kit with style files, macros, and guidelines for this format is linked below. (Note that the 2024 author kit includes a AAAI copyright slug, which should be removed for workshop publications.)
- Papers should be submitted in PDF format, electronically, using the EasyChair submission system. Ensure submitting to Agent4IR workshop.
Workshop Chair:
Qingsong Wen ai4agent@gmail.com
Workshop Committee:
Yongfeng Zhang, Zhiwei Liu, Julian McAuley, Linsey Pang, Wei Liu, Philip S. Yu
Workshop URL:
https://sites.google.com/view/ai4ir/aaai-2025
Contact: ai4agent@gmail.com
W30: AI4Research: Towards a Knowledge-grounded Scientific Research Lifecycle
This workshop aims to help researchers explore and discuss the entire scientific research lifecycle, detailing how machines can augment every stage of the research process, including literature survey, hypothesis generation, experiment planning, results analysis, manuscript writing, paper evaluation, and fact-checking. We expect interdisciplinary collaboration to explore autonomous research for topics beyond existing natural science domains. This workshop solicits viewpoints from scientists and technology developers to look beyond technical issues to better understand the needs of the human-in-the-loop scientific research lifecycle.
Topics
- Challenges and potential solutions for autonomous scientific research
- State-of-the-art algorithms for each stage of autonomous scientific research
- Accessibility to scientists and collaboration between knowledge-grounded autonomous scientific research models
- The social impact and ethical considerations of knowledge-grounded AI methods in the scientific research lifecycle
- Dataset: We will introduce a dataset track to emphasize new tasks in AI4Research.
- Evaluation: We will also accept new evaluation methods or metrics to measure the performance of AI methods for different stages of knowledge-grounded autonomous scientific research, including scientific hypothesis generation, automated review generation, etc.
Format of Workshop
This will be a one-day workshop with at least four keynote talks, a panel discussion, a student mentoring session, presentations of invited and accepted long papers (8 pages) and short papers (4 pages) in AAAI format, and an awards ceremony towards the end.
Attendance
We anticipate an audience of approximately 300 participants, including researchers and industry representatives interested in AI4Research. A significant challenge in this interdisciplinary field is that AI developers and scientists in other disciplines often work on their own problems. Our workshop seeks to unite these researchers and facilitate collaboration to create a more holistic, focused view of AI4Research. This workshop’s call-for-papers aims to bridge researchers from different subcommunities within machine learning and outside machine learning (such as biochemistry, physics, finance, climate change, social science, etc).
Submission Requirements
Authors are invited to submit papers of 4 (short) or 7 (long) pages, with unlimited pages for references and appendices. We invite submission of full-length and short-length papers across two different tracks:
- General Track
- Dataset and Evaluation Track
All papers must be submitted in PDF format, using the AAAI-24 author kit.
The submission deadline is November 22, 2024 (11:59PM AoE).
Submission Site Information
Author will submit at: https://openreview.net/group?id=AAAI.org/2025/Workshop/AI4Research
The submission site will be listed both in the CFP and our workshop website: https://sites.google.com/view/ai4research2024/call-for-papers
Organizing Committee
Qingyun Wang (qingyun4@illinois.edu, primary contact) , Wenpeng Yin (wenpeng@psu.edu ), Lifu Huang (lfuhuang@ucdavis.edu), Yi R. Fung ( yrfung@ust.hk), Xinya Du (xinya.du@utdallas.edu), Carl Edwards (cne2@illinois.edu), Tom Hope (tomh@allenai.org)
Workshop URL
W31: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications
Time series data are becoming ubiquitous in numerous real-world applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, financial markets, biological sciences, environmental sciences, etc. Given the availability of massive amounts of data, their complex underlying structures/distributions, together with the high-performance computing platforms, there is a great demand for developing new theories and algorithms to tackle fundamental challenges (e.g., representation, classification, prediction, causal analysis, etc.) in various types of applications.
The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will focus on both the theoretical and practical aspects of time series data analysis and aims to trigger research innovations in theories, algorithms, and applications. We will invite researchers, AI practitioners, and policymakers from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop.
Topics:
This workshop encourages submissions of innovative solutions for a broad range of time series analysis problems. Topics of interest include but are not limited to the following:
- Time series forecasting and prediction
- Spatio-temporal forecasting and prediction
- Time series anomaly detection and diagnosis
- Time series change point detection
- Time series classification and clustering
- Time series similarity search
- AI-inspired approaches for time series similarity search
- Time series indexing
- Time series compression
- Time series pattern discovery
- Time series pattern discovery
- Interpretation and explanation in time series
- Causal discovery and inference in time series
- Bias and fairness in time series
- Benchmarks, experimental evaluation, and comparison for time series analysis tasks
- Foundation models and LLMs for time series analysis
- Time series applications in various areas: E-commerce,
- Cloud computing, Transportation, Fintech, Healthcare,
- Internet of things, Wireless networks, Predictive maintenance, Education, Energy, Climate, etc.
Important Dates
- December 1, 2024 – Submission Deadline
- December 15, 2024 – Acceptance Notification
- March 4, 2025 – Workshop Date
Attendance:
Researchers, students, and practitioners in AI, machine learning, data mining, and time series analysis.
Submission Requirements:
Submissions should be 4-7 pages long, excluding references, and follow AAAI 2025 template. Submissions are double-blind and author identity should not be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).
Workshop Organizers
- Dongjin Song, University of Connecticut
- Qingsong Wen, Squirrel Ai Learning
- Yao Xie, Georgia Institute of Technology
- Cong Shen, University of Virginia
- Sanjay Purushotham, University of Maryland Baltimore County
- Shirui Pan, Griffith University
- Stefan Zohren, University of Oxford
- Haifeng Chen, NEC Labs America, Princeton
- Yuriy Nevmyvaka, Morgan Stanley
Workshop website
W32: Artificial Intelligence for Wireless Communications and Networking (AI4WCN)
Artificial intelligence (AI)/Machine learning (ML) in networked systems, is envisaged as the cornerstone of next-generation wireless networks. The integration of AI in 6G, in the era of generative AI, is expected to revolutionize net-work operations, support a wide array of intelligent services, and enable new applications that were previously not feasible. Despite its immense potential and emerging applications, several new challenges must be addressed. These challenges include the need for advanced AI models that can handle the heterogeneity of 6G networks, ensuring security and privacy, and developing efficient resource management approaches to support the demands of AI-driven applications. Addressing these issues is crucial for unlocking the full potential of AI in next-generation networks. To that end, this workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches in efficient training and robust deployment of AI/ML models over wireless networks.
Topics
- Distributed AI/ML over wireless networks
- Information freshness in networked AI systems
- Data privacy in AI-enabled wireless networks
- Reliable and trustworthy AI services for wireless systems
- Decision-making in dynamic wireless environment
- Model inference over noisy channels
- Generative AI/foundation models for intelligent networks
- Explainable AI for semantic communications
- Novel methods for learning with limited resources
- Human-in-the-loop methods in AI-enabled networks
- Planned format and attendee
Our half-day workshop will include keynote/invited talks, contributed paper presentations, and a poster session. We aim to accommodate an audience of up to 50 attendees.
The proposed workshop is expected to be comprised of two keynotes, followed by regular paper presentations.
All presentations must be in person. Only virtual attendance will be permitted.
Submission Requirements
- Submissions of technical papers can be up to 7 pages excluding references and appendices. Short or position papers of up to 4 pages are also welcome. All papers must be submitted in PDF format, following the AAAI 2025 format.
- Papers will be peer-reviewed (single-blind) and selected for oral and/or poster presentation at the workshop.
- The contributions will be non-archival but will be hosted on our workshop website.
Submission site: The submission will be conducted through OpenReview. Submission site/URL will be announced later in our workshop website (https://aaai2025-ai4wcn.webflow.io/ ).
Workshop Chair
Howard H. Yang, ZJU-UIUC Institute, Zhejiang University, China, haoyang@intl.zju.edu.cn
Zihan Chen, Singapore University of Technology and Design, Singapore, zihan_chen@sutd.edu.sg
Zuozhu Liu, ZJU-UIUC Institute, Zhejiang University, China, zuozhuliu@intl.zju.edu.cn
Nikolaos Pappas, Linköping University, Sweden, nikolaos.pappas@liu.se
Committee
Final TPC member list is to be determined and will be updated on the workshop website.
Workshop URL
W33: Artificial Intelligence with Causal Techniques
Objective:
Causality aims to describe the principle that certain events cause specific outcomes, helping us understand, predict, and explain changes in the world. Recently, the connection between causality and AI has become increasingly important, where AI can benefit from causal reasoning to build more robust, interpretable, and generalizable models. Therefore, people seek to use AI with causal techniques to benefit various communities like healthcare, e-commerce, and social science.
The Artificial Intelligence with Causal Technologies (AICT) workshop aims to discuss recent advances in causal methodology, including novel causal discovery and causal inference methods, as well as methods for downstream causal tasks such as causal representation learning, causal reinforcement learning, causal fairness, etc. We will also explore how these advances in the causal community can contribute to different subfields of AI such as recommender systems, natural language processing, computer vision, etc. In addition, it is interesting to discuss the intersection of causality and large models, including how large models can be utilized to improve the performance of causal tasks, as well as how causal insights can be used to enhance the reasoning ability and reliability of large models.
Topics:
Topics of interest include, but are not limited to:
- Causal discovery and causal inference;
- Causal representation learning;
- Causal agent and reinforcement learning;
- Causal fairness;
- Causality for safe and explainable AI;
- Causality and large models;
- Datasets and benchmarks on causality;
- Applications of causal techniques in healthcare, e-commerce, social science, etc.
Important Dates:
- Submission Deadline: November 30, 2024
- Notification of Acceptance: December 9, 2024
- Workshop Date: March 4, 2025
All deadlines are specified in Anywhere on Earth (AoE).
Submission Guidelines:
We welcome two types of papers:
- Full papers: Full-length research papers from 4 to 7 pages (excluding references and appendices);
- Short papers: research/position papers of up to 4 pages (excluding references and appendices).
Papers should be submitted in the AAAI format. The review process will be single-blinded, and we welcome accepted and published papers. The contributions will be non-archival and only will be hosted on our workshop website. There will be the oral presentation awards and one best paper award for accepted papers with outstanding quality.
The workshop uses Openreview for paper submission and review. The submission link is https://openreview.net/group?id=AAAI.org/2025/Workshop/AICT.
Attendance:
The workshop is open to researchers, practitioners, and industry professionals interested in AI with causal techniques. There are no specific criteria or maximum number of attendees.
Workshop Format:
This is a one-day workshop with no restrictions on attendance. The workshop is organized to include keynote presentations of invited speakers, panel discussion, oral paper presentations, and poster sessions.
Workshop Chairs:
Haoxuan Li (Main Contact), Peking University, hxli@stu.pku.edu.cn
Zhouchen Lin, Peking University, zlin@pku.edu.cn
Yan Liu, University of Southern California, yanliu.cs@usc.edu
Xiao-Hua Zhou, Peking University, azhou@math.pku.edu.cn
Workshop Organizers (Alphabetical Order):
Yongqiang Chen, Carnegie Mellon University & MBZUAI, lfhasechan@gmail.com
Mingming Gong, The University of Melbourne & MBZUAI, mingming.gong@unimelb.edu.au
Amit Sharma, Microsoft Research, amshar@microsoft.com
Hao Wang, Zhejiang University, haohaow@zju.edu.cn
Jun Wang, University College London, jun.wang@cs.ucl.ac.uk
Mengyue Yang, University of Bristol, mengyue.yang.20@ucl.ac.uk
Hanwang Zhang, Nanyang Technological University, hanwangzhang@ntu.edu.sg
Kun Zhang, Carnegie Mellon University & MBZUAI, kunz1@cmu.edu
Min Zhang, East China Normal University, zhangmin.milab@zju.edu.cn
Chunyuan Zheng, Peking University, cyzheng@stu.pku.edu.cn
Workshop URL:
Workshop URL: https://sites.google.com/view/aict-2025
Workshop Contact: hxli@stu.pku.edu.cn
W34: Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
While AI Planning and Reinforcement Learning communities focus on similar sequential decision-making problems, these communities remain somewhat unaware of each other on specific problems, techniques, methodologies, and evaluations.
This workshop aims to encourage discussion and collaboration between researchers in the fields of AI planning and reinforcement learning. We aim to bridge the gap between the two communities, facilitate the discussion of differences and similarities in existing techniques, and encourage collaboration across the fields. We solicit interest from AI researchers that work in the intersection of planning and reinforcement learning, in particular, those that focus on intelligent decision-making. This is the eighth edition of the PRL workshop series.
Format of Workshop
The workshop will last the complete day of March 4.
It will include a poster session, both long (30 minutes) and short (15 minutes) talks, invited talks, and potentially a panel open for discussion with the audience.
Attendance: No restrictions.
Submission Requirements
We invite submissions at the intersection of AI Planning and Reinforcement Learning. The topics of interest include, but are not limited to, the following
- Model-Based, Hierarchical and Safe Reinforcement Learning
- Monte Carlo planning
- Model representation and learning for planning
- Planning using approximated/uncertain (learned) models
- Learning to Search
- Theoretical aspects of planning and RL
- Action policy analysis or certification
- RL and Planning competition(s), datasets, and benchmarks
- Multi-agent planning and learning
- Applications combining RL and Planning
- Integration of planning and RL for hierarchical approaches
- Integrating Planning and RL for exploration (Planning-based exploration in RL)
- Combining RL and Planning for interpretability and explanations
We solicit workshop paper submissions relevant to the above call of the following types:
- Long papers – up to 8 pages + unlimited references/appendices
- Short papers – up to 4 pages + unlimited references/appendices
- Extended abstracts – up to 2 pages + unlimited references/appendices
Format
Papers must be formatted in AAAI two-column, camera-ready style; see the AAAI-25 author kit for details.
Important Dates:
- Paper submission deadline: Sunday, November 24, 2024 (AOE)
- Paper acceptance notification: Monday, December 9, 2024 (AOE)
Submission Site Information
OpenReview. The request for a submission site has already been made, but the link has not yet been provided by OpenReview. It will be provided as soon as possible.
Workshop Chair & Workshop Committee
- Zlatan Ajanović, RWTH Aachen, Aachen, Germany
- Timo P. Gros, German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
- Floris den Hengst, University of Amsterdam, Amsterdam, Netherlands
- Daniel Höller, Saarland University, Saarbrücken, Germany
- Harsha Kokel, IBM Research, San Jose, USA
- Ayal Taitler, Ben-Gurion University, Be’er Sheva, Israel
Please send your inquiries to prl.theworkshop@gmail.com
Workshop URL
Please refer to the PRL workshop website for the latest information.
W35: CoLoRAI – Connecting Low-Rank Representations in AI
The Connecting Low-Rank Representations in AI (CoLoRAI) workshop aims to bring together researchers from diverse fields, including AI, machine learning, optimization, and quantum computing, to explore the common ground in utilizing low-rank representations for complex problem-solving. Recent advancements in AI applications, such as tensor factorizations and polynomial networks, have enabled improvements in fields like large language models, quantum computing, and deep learning. The goal of this workshop is to foster collaboration and dialogue across different research communities working with low-rank methods to drive further breakthroughs in AI.
Topics
- Low-rank structures to speed up computation of large ML systems such as adaptors in LLMs and structured computational graphs;
- Circuit representations for reliable and efficient probabilistic reasoning and learning with applications to trust- worthy ML such as reliable neuro-symbolic AI;
- Tensorial architecture design in deep learning such as polynomial representations that have emerged as a strong-performing alternative to the standard neural net- work paradigm;
- Tensor networks for quantum and physics-inspired computing to solve variational inference, PDEs and in- verse problems;
- Theory of matrix and tensor factorization methods and their optimization in sketching, compression and tensor completion;
- Expressivity of tensor representations including exponential lower bounds of matrix ranks and circuit sizes and sample complexity of learned representations;
- Applications of low-rank representations in AI-related fields such as data and model compression, multimodal fusion, and model interpretability.
Workshop Format
This one-day workshop will include Invited talks, spotlight presentations, two poster session and panel discussion. There is no attendance maximum number of attendees.
Submission Requirements
Two types of papers are welcomed: Research papers (up to 4 pages in AAAI format, excluding references). Papers already accepted at top ML and AI conferences in the last year.
All submissions will be peer-reviewed through OpenReview. Link: https://openreview.net/group?id=AAAI.org/2025/Workshop/CoLoRAI
Organizing Committee
Antonio Vergari (University of Edinburgh, UK)
Grigorios Chrysos (University of Wisconsin-Madison, US)
Chao Li (RIKEN-AIP, Japan)
Deanna Needell (University of California, Los Angeles, US)
Workshop URL
W36: Computational Jobs Marketplace
The Second International Workshop on Computational Jobs Marketplace to be held as part of The 39th Annual AAAI Conference on Artificial Intelligence
Online job marketplaces such as Indeed.com, ZipRecruiter, CareerBuilder and LinkedIn Inc. help millions of job seekers find their next job. These platforms also provide services for thousands of employers to fill their opening positions. With all players in the ecosystem, the market size of this industry is projected to steadily grow and reach $43 billion dollars in 2027. On top of that, the global pandemic COVID-19 in 2020 and the emerging AI trends have profoundly transformed workplaces and the online jobs marketplace, creating and driving new types of jobs and marketplace technologies around the world. Today, online job marketplaces play a central role in this new wave of digital revolution of workforce and workplaces. While this industry generates tremendous growth in the past several years, technological innovations around this industry have yet to come. Many technologies, such as search systems, recommender systems as well as advertising systems that the industry heavily relies on are deeply rooted in their more generic counterparts, which may not address the unique challenges in this industry for better products serving both job seekers and employers/recruiters.
This workshop would play a critical role to bring together the research and development community in this industry especially around data science and machine learning and facilitate innovations on theories, models, systems and practices in this currently scattered community. An expected outcome of the workshop is to create awareness of this emerging industry with its technological opportunities and challenges, which might foster future research and development, creating novel products to serve future job seekers and employers/recruiters.
Topics
We solicit papers describing significant and innovative research and applications to the field of job marketplaces. We invite submissions on a wide range of topics, spanning both theoretical and practical research and applications. Topics include but not limited to:
- Job search technologies
- Large-scale and novel targeting technologies
- User and job understanding
- Recommendation systems
- Budget control and optimization
- Ranking and scoring systems
- Marketplace experiments and A/B testing
- Creative optimization
- Fraud, fairness, explainability and privacy
- Intelligent assistants in job hunting and hiring automation
- Large-scale and high performing data infrastructure, data analysis and tooling
- Deployed systems and battle scars
- Economics and causal inference in online jobs marketplace
- Large-scale analytics of user behaviors in online jobs marketplace
- Large Language Models (LLM) for online jobs marketplace
- Blockchain in online job marketplace
Important Dates
- November 22, 2024: Submissions Due
- December 9, 2024: Notifications Out
- March 4, 2025: Workshop Day
Submission Details
Following the AAAI 2025 main conference submission & review process, each paper will be reviewed by PC members. The acceptance decisions will take in account novelty, technical depth and quality, insightfulness, depth, elegance, practical or theoretical impact, reproducibility and presentation. We will use double-blind reviewing. For each accepted paper, at least one author must attend the workshop and present the paper.
Submissions are limited to a total of FIVE pages, including all content and references, must be in PDF format, and formatted according to the AAAI standard. Additional information about formatting and style files is available here.
Organizers
- Mohammed Korayem, CareerBuilder, mkorayem@gmail.com
- Haiyan Luo, ZipRecruiter, itshaiyan@gmail.com
- Liangjie Hong, LinkedIn Inc., hongliangjie@gmail.com
W37: DEFACTIFY 4.0 – Workshop Series on Multimodal Fact-Checking and Hate Speech Detection
More information to come
W38: FLUID: Federated Learning for Unbounded and Intelligent Decentralization
The FLUID: Federated Learning for Unbounded and Intelligent Decentralization workshop focuses on overcoming the challenges of adaptability, scalability, and decentralization in Federated Learning (FL) systems. As FL continues to evolve, its applications in highly heterogeneous environments—such as healthcare, smart cities, and autonomous systems—demand novel solutions to ensure resilience and performance in dynamic, decentralized contexts. The workshop aims to bridge the gap between theoretical advancements and real-world deployment, focusing on practical methodologies, tools, and case studies.
Topics
- Novel algorithms for handling statistical and device heterogeneity in FL
- Scalability and robustness in decentralized FL systems
- Adaptive learning frameworks for dynamic and non-stationary environments
- Practical applications of FL in healthcare, autonomous systems, finance, smart cities, and IoT networks
- Fairness and bias mitigation in FL across heterogeneous data sources
- Benchmarking and evaluation methods for real-world FL deployments
- Communication-efficient FL algorithms for resource-constrained environments
- Real-world case studies and success stories of FL deployment
- Tools and platforms for deploying FL systems at scale
- Integration of FL with edge computing and multi-agent systems
Format and Attendance
This 1-day workshop will consist of keynote talks by prominent experts and paper presentations in thematic sessions. The program will also include interactive discussions and poster presentations of high-quality submissions not selected for oral sessions. If applicable, a Best Paper Award may be presented at the workshop’s conclusion. The workshop anticipates 30 participants, including academic researchers and industry professionals. There are no specific attendance criteria beyond the general AAAI registration requirements. The maximum number of attendees will be determined by the room capacity. Other AAAI attendees who are interested can also attend following AAAI’s related policy.
Submission Requirements
We accept both short papers (up to 4 pages) focused on emerging ideas or preliminary results and long papers (up to 7 pages) offering in-depth contributions related to the workshop themes. All submissions should adhere to the AAAI formatting guidelines and will undergo a double-blind review process.
Submission Site Information: https://cmt3.research.microsoft.com/FLUID2025
Workshop Chair/Committee
Dr. Diletta Chiaro, diletta.chiaro@unina.it (primary contact chair)
Prof. Francesco Piccialli, francesco.piccialli@unina.it
Prof. Shadi Albarqouni, shadi.albarqouni@ukbonn.de
Prof. David Camacho, david.camacho@upm.es
Workshop URL
For more details, visit https://fluidworkshop.github.io/
W39: Generalization in Planning
Finding solutions to sequential decision-making (SDM) problems that generalize across problem instances and domains is crucial to the advancement of artificial intelligence (AI). Generalized solutions broaden access to AI algorithms, reduce resource consumption, and enable knowledge discovery at a broad scale. Recent advances in deep reinforcement learning and generative AI have led to data-driven methods that are effective for short-horizon reasoning and decision-making, with open problems regarding sample efficiency, guarantees of correctness, and applicability to long-horizon settings. On the other hand, the AI planning community has made complementary strides, developing robust analytical methods that enable sample-efficient generalization and transferability in long-horizon planning, with open problems in designing and modeling representations. This workshop aims to unify relevant research that is often fragmented across separate research communities, including AI planning, deep learning, reinforcement learning, logic programming, model learning, and robotics.
Topics
Formalizations of generalizable SDM; representation, learning, and synthesis of generalizable solutions, high-level models, and hierarchical policies; transfer learning paradigms and algorithms; synthesizing generalized Q/V-functions and heuristics; few-shot learning; learning for program synthesis; etc.
Related techniques: deep learning; reinforcement learning; inductive logic programming; classical planning; heuristic search; and neuro-symbolic approaches.
Format of Workshop
One-day; will feature presentations of contributed papers, invited plenary talks, panel discussions, and poster sessions. No restrictions on attendance.
Submission Requirements
We invite submissions on work in progress and mature research, including “highlight” papers summarizing recent results from multiple papers by the authors. Preference will be given to new and ongoing work over exact resubmissions of previously published work.
Paper submissions (AAAI style format) are of two types:
- Full technical papers: Up to 7 pages + unlimited references/appendices
- Short papers: Between 3 and 5 pages + references/appendices
Accepted papers will be available digitally on the workshop’s website. Some long papers will be selected for talks; all papers will be presented as posters. Papers under review at other venues are welcome since GenPlan is non-archival and does not require copyright transfer.
Submission Site Information
Openreview link for the submission can be found on the workshop website. Please feel free to send queries at: genplan25aaai@gmail.com.
Workshop Committee
Rashmeet Kaur Nayyar, Arizona State University, rmnayyar@asu.edu (Main Contact) Naman Shah, Brown University, naman_shah@brown.edu
Misagh Soltani, University of South Carolina, msoltani@email.sc.edu
Abhinav Bhatia, University of Massachusetts Amherst, abhinavbhati@umass.edu Forest Agostinelli, University of South Carolina, foresta@cse.sc.edu
Workshop Website
W40: Workshop and Challenge on Anomaly Detection in Scientific Domains
Description
Scientific discovery often involves the observation of an inconsistency among “normal” patterns within data. Recognizing something different, incongruous with the data, is what we call anomaly detection, which differs from other tasks since we do not know what exactly to look for—just to look for something different. This workshop aims to nurture the community of researchers at the intersection of machine learning and various scientific domains.
Additionally, we will hold the award ceremony for the 1st HDR Interdisciplinary Machine Learning Challenge (nsfhdr.org/mlchallenge). This anomaly detection competition comprises one challenge in each of biology, physics, and climate science, with a combined challenge across domains. Integration of FAIR and Reproducible science is a critical element.
Topics
We encourage participation from researchers in a broad range of topics exploring AI/ML techniques to detect novel patterns and anomalies within data and promote scientific discovery. Research questions may include (but are not limited to): How can we automate the discovery of new scientific phenomena? How can we embed a notion of uncertainty within AI/ML to quantify the significance of a discovery? How do we ensure FAIR reproducibility of these discoveries? Which interpretable AI/ML approaches can develop direct explanations of these discoveries?
Format
Our 1-day workshop will include keynote/invited talks, contributed paper presentations, a poster session, a panel discussion, and presentations by the challenge winners.
Attendance
The intended audience includes AI/ML/data science researchers working on topics such as anomaly or out-of-distribution detection, open world recognition, scientific discovery, and FAIR dataset and reproducible workflows, looking for novel interdisciplinary research problems; domain scientists working on data-driven scientific discoveries.
Submission Requirements
We are accepting extended abstract submissions for position, review, or research results (2 pages, excluding references). Shorter versions (6 pages, excluding references) of articles accepted at other venues are acceptable provided it does not violate their dual-submission policy. All submissions will undergo peer review and authors may publish in arXiv proceedings.
Submission Site Information https://www.nsfhdr.org/AAAI-workshop
Organizing Committee
- Wei-Lun Chao (The Ohio State University, chao.209@osu.edu, chair)
- Philip Harris (Massachusetts Institute of Technology, pcharris@mit.edu)
- Elizabeth Campolongo (The Ohio State University, campolongo.4@osu.edu)
- Yuan-Tang Chou (University of Washington, ytchou@uw.edu)
- Katya Govorkova (Massachusetts Institute of Technology, katyag@mit.edu)\
- Hilmar Lapp (Duke University, hilmar.lapp@duke.edu)
- Josephine Namayanja (University of Maryland, Baltimore County, jona1@umbc.edu)
- Aneesh Subramanian (University of Colorado Boulder, aneeshcs@colorado.edu)
Workshop URL
W41: Knowledge Graphs for Health Equity, Justice, and Social Services
We are glad to announce that we are organizing the 1st workshop on Knowledge Graphs for Health Equity, Justice, and Social Services at AAAI 2025. If you are interested, we sincerely invite you to share your project with us and the broader community via submitting paper contributions (including long, short, and demo papers) to our workshop.
Introduction
Knowledge graphs (KGs) are prevalent in many real-world diverse applications across science and industry including search engines, recommendation systems, natural language processing (NLP), healthcare and life sciences, social networks, smart cities, education, and more. In recent years, KG construction and learning have grown into an established sub-field of AI and foundation models (FMs), i.e., researchers have been focusing on developing novel ontology design and entity identification, reasoning/embedding algorithms, and query answering. On the other hand, with the rising awareness that health equity and social justice are important, there are several challenges that public health professionals face. These include widespread inequities, structural racism and discrimination, geographic disparities, substance use and social inequities, etc. Additionally, learning algorithms and systems have exposed vulnerabilities, e.g., bias in AI systems is a critical issue that affects fairness, equality, and trust and leads to unfair outcomes; data problems (e.g., curation of training data) significantly impact the performance, fairness, and reliability of FMs. In order to promote health equity, advance justice, and dismantle barriers to personalized services to society, especially marginalized communities, it has become critical that the communities related to AI, FMs, KGs, and social science join their forces in order to develop more high-quality data and effective algorithms and applications. Our workshop aims to provide an opportunity for scientific researchers, field practitioners, government agencies, legal services, and industrial partners to be at the forefront of this transformative initiative. This workshop will bring together experts from diverse fields, including but not limited to AI, FMs, KGs, social work, public health, justice, and health services research, to create a dynamic platform for brainstorming, collaboration, and action.
Important Dates
- Workshop paper submission deadline: November 11th, 2024 (AOE).
- Notification to authors: December 9th, 2024 (AOE).
- Date of workshop: March 4th, 2025.
Topics
Topics of interest include, but are not limited to:
- KG and AI applications in advancing health equity and social justice
- KG with Social Determinants of Health (SDOH): Mapping, Visualization, and integrating SDOH data
- Knowledge graph construction and retrieval to uncover health and service disparities
- Predictive modeling and machine learning using knowledge reasoning, extraction, and integration
- Ethical considerations and biases in AI, FMs, and KG Applications
- Successful implementations, case studies, and initiatives that integrated KGs and AI for health and justice
- Technical, ethical, and logistical challenges in applying KGs, AI, and FMs to social science
- Designing interventions and policies based on KG and AI insights
- Knowledge graph enhanced large language models (LLMs) and retrieval-augmented generation (RAG) and their applications in social science
- Relational and graph based reasoning and multimodal learning with knowledge graphs
Submission
We welcome contributions of long (7 pages), short (4 pages), and demo (4 pages) papers related to our stated vision in the AAAI 2025 proceedings format. Position papers and surveys are also welcome. All contributions will be peer reviewed (single-blind). Paper submission link will be released soon.
Publication and Attendance
All accepted papers will be given the opportunity to be presented in the workshop. The accepted papers will be posted on the workshop’s website. These non-archival papers and their corresponding posters will remain available on this website after the workshop. The authors will retain copyright of their papers. Virtual and Remote Attendance will be available to everyone who has registered for the workshops. The workshop will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center on Mar. 4, 2025.
Registration
All attendees need to register for the workshop. Please check more details about AAAI 2025 workshop registration: https://aaai.org/conference/aaai/aaai-25/registration/.
Please contact yuzhouc@ucr.edu with any further questions.
Chairs and Organizers
Yuzhou Chen, University of California, Riverside
Huanmei Wu, Temple University
Jiaqi Gong, The University of Alabama
Omar Martinez, University of Central Florida
Workshop Website
W42: Large Language Model and Generative AI for Health
The rapid evolution of Generative AI, Large Language Models (LLMs), and multimodal models is reshaping the landscape of healthcare. These advanced AI models, when integrated with diverse data types such as clinical notes, medical images, and electronic health records (EHRs), hold immense potential to revolutionize diagnostics, treatment planning, and patient management. This workshop will bring together experts to explore the transformative role of AI in healthcare while addressing the critical challenges that come with it.
Despite their promise, the adoption of LLMs and Generative AI in healthcare is not without obstacles. Issues around fairness, trust, clinical validation, and bias mitigation are central to this discussion. How can we ensure that these models are transparent, ethical, and comply with regulatory standards? What strategies can mitigate inherent biases and build trust with both clinicians and patients?
This workshop will foster interdisciplinary collaboration between AI researchers, healthcare professionals, and policymakers. It aims to bridge the gap between cutting-edge technological innovations and real-world clinical practice, ensuring that AI-driven healthcare is effective, trustworthy, and accessible to all patients.
Topics
We invite high-quality submissions in (but not limited to) the following areas:
- Large Language Models (LLMs) for Clinical Data: Natural language processing of medical notes, EHRs, and patient narratives using LLMs.
- Generative AI in Medical Imaging: Applications of generative models for interpreting and enhancing medical images (e.g., X-rays, MRI, CT scans).
- Multimodal AI for Healthcare: Combining text, image, and structured data for comprehensive patient assessments and decision support.
- Clinical Decision Support Systems (CDSS): AI-based systems that assist healthcare providers in diagnosis and treatment planning.
- Ethics and Fairness in AI: Methods for addressing bias, fairness, and explainability in AI models in clinical settings.
- Trust and Transparency: Building trustworthy AI systems that clinicians and patients can rely on for accurate diagnosis and care.
- Regulatory Compliance and Standards: Challenges and solutions in ensuring that AI models comply with healthcare regulations and ethical standards.
- Real-World Applications: Case studies and implementations of LLMs and generative AI in real-world healthcare settings.
- AI for Public Health: Applications of AI in large-scale health monitoring, disease outbreak prediction, and population health management.
- Interdisciplinary Collaboration: Best practices for fostering collaboration between AI researchers, healthcare providers, and policy makers.
Format
TBD
Submission Guidelines
We invite original research papers, position papers, and case studies. Submissions should align with the workshop’s focus on advancing AI in healthcare while addressing ethical, technical, and clinical challenges.
- Paper Length: Submissions should be up to 6 pages (excluding references), following the AAAI 2025 paper format.
- Submission Deadline: Sunday, November 24th, 2024
- Notification of Acceptance: Monday, December 9th, 2024
- Camera-Ready Submission: TBD
Submission Link
https://openreview.net/group?id=AAAI.org/2025/Workshop/GenAI4Health
Important Dates
- Submission Deadline: Sunday, November 24th, 2024
- Notification of Acceptance: Monday, December 9th, 2024
- Workshop Date: AAAI 2025 (Exact Date- March 4th, 2025)
Organizers
Core Organizers
- Kaidi Xu, Assistant Professor, Depzartment of Computer Science, Drexel University (kx46@drexel.edu )
- Pengtao Xie, Assistant Professor, University of California San Diego (p1xie@ucsd.edu)
- Divya Chaudhary, Assistant Professor, Khoury College of Computer Sciences, Northeastern University (d.chaudhary@northeastern.edu)
Organizers/ Organizing Committee
- Rahul Krishnan, Assistant Professor, University of Toronto (rahulgk@cs.toronto.edu)
- Judy Gichoya, Associate Professor, Emory University (judywawira@emory.edu)
- Monica Agrawal, Assistant Professor, Duke University (monica.agrawal@duke.edu)
- Eric Xing, Professor, Mohamed bin Zayed University of Artificial Intelligence & Carnegie Mellon University (epxing@cs.cmu.edu)
- Bethany Edmunds, Professor and Assistant Dean, Khoury College of Computer Sciences, Northeastern University (b.edmunds@northeastern.edu) Rajiv Ratn Shah, Associate Professor, IIIT- Delhi (rajivratn@iiitd.ac.in)
- Sahil Wadhwa, Data Science Manager, Capital One (sahil24wadhwa@gmail.com)
- Halil Kilicoglu, Associate Professor, School of Information Sciences, University of Illinois at Urbana-Champaign (halil@illinois.edu)
- Ying Ding, Bill & Lewis Suit Professor, School of In- formation, University of Texas (ying.ding@austin.utexas.edu )
- Jon Tamir, Assistant Professor, Chandra Department of Electrical and Computer Engineering, University of Texas (jtamir@utexas.edu)
- Justin Rousseau, Associate Professor, University of Texas Southwestern Medical Center.
- Huanmei Wu, Professor and Chair, Department of Health Services Administration and Policy, Temple University’s College of Public Health. (huanmei.wu@temple.edu)
- GQ Zhang, Professor, Department of Neurol- ogy, McGovern Medical School and Co-Director, Texas In- stitute for Restorative Neurotechnology, Vice President and Chief Data Scientist for UTHealth.(Guo-Qiang.Zhang@uth.tmc.edu)
- Pranav Rajpurkar, Assistant Professor, Harvard with appointments in the Department of Biomedical Infor- matics, Blavatnik Institute, Broad Institute of MIT and Har- vard, and Harvard Data Science Initiative (pranav_rajpurkar@hms.harvard.edu)
- Qi Long, Professor, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pensylvania (qlong@pennmedicine.upenn.edu)
- Li Shen, Professor, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (li.shen@pennmedicine.upenn.edu)
Workshop Website
https://sites.google.com/view/genai4health-aaai-2025
For any questions or further details, please contact us at d.chaudhary@northeastern.edu
We look forward to your contributions and to an engaging discussion on the future of AI in healthcare!
W43: Machine Learning for Autonomous Driving
The workshop “Machine Learning for Autonomous Driving” (ML4AD) is an event for artificial intelligence and machine learning researchers to discuss research problems concerning autonomous driving (AD). Our goal is to promote AI/ML research, and its real-world impact, on self-driving technologies. The fundamental question is “How does AI/ML impact and advance AD in different aspects?”. Full self-driving capability (“Level 5”) is far from solved and extremely complex, beyond the capability of any one institution or company, necessitating larger-scale communication and collaboration.
Since 2016, ML4AD has been a leading workshop at the intersection of machine learning, artificial intelligence, and autonomous driving. This workshop brings researchers from academia, industry, and government together to discuss the latest advancements and foster collaboration in this rapidly-evolving field. The atmosphere is collaborative and engaging, highlighting cutting-edge research and innovative ideas.
Topics
- Prediction and Planning for AD with LLMs
- Foundation Models for AD
- Mapless Autonomous Driving
- Scaling Laws for AD
- Diffusion modeling for prediction and planning
- Closed loop training and evaluation
- Causal/counterfactual analysis of interactive multi-agent scenarios
- Real-time inference and prediction
- Data-driven AD simulation
- Human driver-in-the-loop for interaction modeling
- Coordination with vehicles (V2V) or infrastructure (V2I)
- Uncertainty propagation through AD software pipelines
- Imitation learning and reinforcement learning for AD
- Off-road autonomous driving
- Adaptive driving styles based on user preferences
- Metrics/benchmarks for AD
Format of Workshop
ML4AD will be a 1-day workshop. The goal of the workshop is to promote idea-sharing and collaboration between attendees. The workshop will consist of lightning presentations as introductions prior to the poster sessions; (2) keynote talks to communicate the state-of-the-art; (3) a panel discussion to discuss future research directions; (4) social breaks for researchers to discuss ideas and meet other workshop participants.
Attendance
All AAAI attendees who are interested in the intersection of machine learning and autonomous driving are welcome.
Submission Requirements
Papers should be anonymized; reviewing is double-blind. The requirements are analogous to AAAI requirements, i.e., 7 pages of technical content plus additional pages solely for references; acknowledgements should be omitted from papers submitted for review. Code and other supplementary materials can optionally be submitted; if using GitHub, please ensure that the repository is anonymous and has no information, including commit messages, that will break anonymity.
Submission Site Information
Please see detailed submission instructions, including format requirements at https://ml4ad.github.io. Submit via CMT at https://cmt3.research.microsoft.com/ML4AD2025.
Workshop Chair
Paul Tylkin (Toyota Research Institute), paul.tylkin@tri.global
Workshop Committee
Maximilian Naumann (Bosch), Maximilian.Naumann@de.bosch.com
Madhumitha Sakthi (Qualcomm), msakthi@qti.qualcomm.com
Jiachen Li (University of California, Riverside), jiachen.li@ucr.edu
Aman Sinha (Waymo), thisisaman@waymo.com
Marco Pavone (NVIDIA & Stanford University), pavone@stanford.edu
Rowan McAllister (Waymo), mcallister@waymo.com
Workshop URL
W44: MALTA: Multi-Agent Reinforcement Learning for Transportation Autonomy
This workshop will explore the challenges and opportunities of Multi-Agent Reinforcement Learning (MARL) in the context of autonomous transportation systems. It aims to address critical issues such as coordination, cooperation, scalability, and real-time decision-making among multiple autonomous agents in complex, real-world transportation environments. The workshop will cover topics including traffic optimization, fleet management, and intelligent infrastructure, bringing together experts from academia and industry to discuss the latest advancements and practical applications of MARL.
Topics
Topics of interest include MARL algorithms for autonomous transportation, encompassing cooperative and competitive approaches, traffic optimization, congestion management, fleet management, and autonomous vehicle coordination. This also involves MARL-based decision-making, planning, and communication strategies for autonomous agents. Further topics include transfer learning, generalization across transportation scenarios, safety and ethical considerations, and the scalability and real-time adaptation of MARL algorithms. Hybrid approaches combining MARL with deep learning, game theory, and other techniques are also of interest, along with simulations, real-world deployment, urban mobility, logistics, and ride-sharing applications. Evaluation metrics, benchmarks for performance assessment, human-agent interactions, and ensuring the robustness and resilience of MARL algorithms in uncertain or adversarial environments are key areas of focus as well.
Format of Workshop
The workshop will be a one-day meeting comprising keynote talks from researchers in the field, contributed talks, spotlight talks and a poster session where contributing paper presenters can discuss their work. Attendance is open to all registered participants.
Attendance
Anyone with interest in the workshop, and registered for AAAI workshops, is welcome to attend.
Submission requirements
Either extended abstracts (4 pages) or full papers (7 pages) anonymized using the AAAI 2025 style guidelines, not including references and supplementary can be submitted. We welcome articles currently under review or papers planned for publication elsewhere as extended abstracts.
Submission Site Information
https://openreview.net/group?id=AAAI.org/2025/Workshop/MALTA
Workshop Chairs
Vaneet Aggarwal, Purdue University, vaneet@purdue.edu
Carlee Joe-Wong, CMU, cjoewong@andrew.cmu.edu
Zhiwei (Tony) Qin, Eva AI (foreva.ai), zq2107@caa.columbia.edu
Satish Ukkusuri, Purdue University, sukkusur@purdue.edu
Workshop URL
W45: Neural Reasoning and Mathematical Discovery — An Interdisciplinary Two-Way Street
Neural architectures are playing an increasing role in AI-assisted mathematical discovery. These architectures can guide theorists in discovering novel mathematics through conjecture generation and autoformalization. Besides mathematical and scientific discovery, the success of neural networks is witnessed in various other domains, e.g., human-like question-answering, playing games, and solving IMO tasks. However, accompanied by these exciting successes are LLMs’ unpredictable behaviours and errors in simple abstract reasoning. This presents an opportunity to develop pipelines for human-like, rigorous, logical reasoning, supported by advances in neural architectures. Recent research shows first glimpses of achieving syllogistic reasoning without training data through the use of sphere neural networks. This workshop invites theorists and practitioners to reconsider various problems and discuss walk-round solutions in the two-way street commingling of neural networks and mathematics (1) using mathematics to develop novel neural networks that can reach the rigour of logical reasoning, and (2) using neural networks to discover and enlighten novel results or paradigms in the mathematical sciences.
Topics
- Mathematical foundations for neural reasoning of mathematical discovery System 2
- Novel neural models with better performance and less data-hungry and cheap-training costs for high-level rational reasoning, e.g., commonsense reasoning, spatial reasoning, logical reasoning, multimodal reasoning; beyond the statistical paradigm
- Reliable AI applications (e.g., explainable, deterministic, safe)
- Interdisciplinary perspectives of neural reasoning
- Cognitive modeling scientific discovery
- Industrial demands with case studies of neural- and neurosymbolic- reasoning
- LLMs and Foundation Model
Format of Workshop
This is a one-day workshop, organized in the form of keynote speeches, invited talks, paper and post presentations, and panel discussions.
The workshop’s attendees are keynote speakers, authors whose papers are accepted, and researchers and industrial practitioners who can attend following AAAI’s related policy. The expected number of attendees is about 55.
Submission Requirements
Full-length research papers up to 7 pages, position or short papers up to 4 pages (excluding references and appendices)
Submission Site: URL to be announced at the Workshop webpage.
Workshop Chairs
Challenger Mishra cm2099@cam.ac.uk (the chairman)
Mateja Jamnik mj201@cam.ac.uk
Pietro Lio pl219@cam.ac.uk
Tiansi Dong tdong@acm.org (the primary contact chair)
Workshop Committee
The Program Committee will be announced at the Workshop webpage.
Workshop URL
W46: Open-Source AI for Mainstream Use
According to the 2024 AI Index Report, 65.7% of the 149 foundation models released in 2023 were open source and there were 1.8 million AI-related projects on GitHub in 2023, a 59.3% rise in just one year. Typical reasons for adopting open models are faster access to innovation, cost effectiveness, transparency, and the ability to modify the model. In addition to foundation models, an open-source AI ecosystem must also include tools and techniques to support downstream activities (e.g. model adaptation, human alignment, testing & evaluation, etc.). With the increasing number of AI regulations around the world that attempt to specify what is acceptable for societal use, how the open-source AI ecosystem manages the risk of building, deploying and managing these systems matters immensely. Therefore, while bringing many economic and social benefits, there are many technical challenges to create an open-source AI ecosystem. The goal of this interdisciplinary workshop is to explore the following five areas:
Topics
- Unique aspects of open source that make them ideal to build responsible AI applications.
- Technology challenges to make open-source AI the mainstream platform.
- Demonstration of the real progress already made in the open-source AI community.
- Technical guidance to support practical and meaningful regulations that promote open technology.
- Building a vibrant open-source AI community and ecosystem.
Due to the importance of practical aspects in these areas, we want to address both active research areas and the practical implementations that shed light on the increasing role of open-source AI in society.
Examples of specific research and demonstration topics are given at the Workshop website.
Format
This one-day workshop will include a keynote, a panel, research presentations, hands-on demonstrations and a poster session.
Attendance
We expect 50-75 participants to support the following types of contributions related to Open Source AI:
- Research papers
- Policy/Position papers
- Posters (maximum 10)
- Hands on Demonstrations
Submission Requirements
We accept the following submission types as pdf documents in the AAAI format.
- Research papers (8 pages)
- Policy/Position papers (4 pages)
- Posters on Research topics (4 pages)
- Hands on Demonstrations (2 pages)
The pages include references and any appendices. More guidance on the submissions is at the Workshop website. The review process will be single blind.
Submission Site
https://easychair.org/conferences/?conf=osai4mu25
Chair Organizer
Peter Santhanam (pasanth@us.ibm.com), IBM Research, USA
Workshop Program Committee
Alexandru Cioba (alexandru.cioba@mtkresearch.com), MediaTek Research, UK.
James Hendler (hendler@cs.rpi.edu), Rensselaer Polytechnic Institute, USA
Serdar Kadioglu (serdark@cs.brown.edu ), Fidelity Investments & Brown University, USA
Ezequiel Lanza (ezequiel.lanza@intel.com), Intel, Canada.
Greg Lindahl (greg@commoncrawl.org), Common Crawl Foundation, USA.
Sujee Maniyam (sujee@node51.com), Node51 LLC, USA.
Manish Parashar (manish.parashar@utah.edu), University of Utah, USA.
Pushkar Singh (pushkarsingh@google.com), Google, USA.
Jonathan Starr (jon@numfocus.org), NumFOCUS, USA.
Raphaël Vienne (raphael.vienne@datacraft.paris), Datacraft, France.
Workshop URL
Open source AI for Mainstream Use https://the-ai-alliance.github.io/AAAI-25-Workshop-on-Open-Source-AI-for-Mainstream-Use/
W47: Scalable and Efficient Artificial Intelligence Systems
As the AI community advances in developing human-like algorithms, it is crucial to understand their implications for scalable and efficient AI systems. While AI excels at small-scale data tasks, managing large-scale, dynamically growing datasets presents new challenges. Addressing these requires collaboration between academia and industry, focusing on both fundamental research and applied technologies. To this end, we introduce the first workshop on Scalable and Efficient Artificial Intelligence Systems (SEAS), a forum for experts to share experiences in designing and developing robust computer vision (CV), machine learning (ML), and AI algorithms, translating them into real-world solutions. SEAS aims to foster collaboration between academics and industry professionals, discussing AI models that efficiently scale with growing data.
Topics
We invite the submission of original and high-quality research papers in the topics related to intersection of efficient training and large-scale deployment of AI systems. The topics for SEAS 2025 include, but are not limited to (please see the workshop website for more topics of interest):
- Algorithms and theories for efficient model training and operational scalability.
- Sector-specific designs for scalable AI, such as telecom, e-commerce, medical imaging, etc.
- Strategies for managing scalable training when new data is integrated.
- Cross-field research insights to improve data handling in scalable AI systems.
Format of Workshop
This 1-day workshop will include invited talks from keynote speakers, and oral/poster presentations of the accepted papers.
Attendance
We expect 50-75 participants and potentially more according to our past experiences. We warmly invite researchers, practitioners, and students from both academia and industry to join us in exploring and discussing solutions for addressing the challenges of scalability and efficiency in AI.
Submission Requirements
- We welcome researchers to submit 7 pages for full papers (excluding references and appendices). All papers will undergo a peer-review process.
- The paper submissions must be in pdf format and strictly use the AAAI official templates. All submissions must be anonymous and conform to AAAI standards for double-blind review.
- The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings.
- At least one author of each accepted submission must present the paper in person at the workshop. Only virtual attendance will be permitted.
Please check our workshop website for detailed submission instructions and timeline.
Submission Site Information
https://cmt3.research.microsoft.com/SEAS2025
Workshop Chair
Abhishek Aich (aaich001@ucr.edu)
Workshop Committee
Abhishek Aich (NEC Laboratories, America, aaich001@ucr.edu), Yumin Suh (NEC Laboratories, America, yumin@nec-labs.com), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories, kp388@cornell.edu)
Workshop URL
W48: Towards Knowledgeable Foundation Models
In this workshop, we want to bring together researchers who focus on different stages and different aspects (structured knowledge, unstructured knowledge, and knowledge acquired from LMs themselves) of the knowledge lifecycle to discuss the role of knowledge in the era of large language models.
Topics
This workshop examines the lifecycle of knowledge within language models: (1) the emergence of knowledge through language model pre-training; (2) injection of external knowledge; (3) the updating and modification of knowledge; (4) probing and generation of knowledge. The knowledge can be multimodal. We welcome submissions related to:
- Analysis of knowledge within FMs: how much they know and where that knowledge is from.
- Enhancing FMs with existing knowledge sources (knowledge graphs, domain-specific databases, manuals, and rules, etc, either during training or inference).
- Analyzing and improving RAG (retrieval-augmented generation) systems Updating and editing knowledge in FMs.
- Knowledge extraction and generation using FMs Evaluation of knowledge utilization (faithfulness, truthfulness) by FMs.
- Identification and mitigation of FM hallucinations, factual error correction
Workshop Format
Our 1-day workshop will include keynote/invited talks, oral presentations, poster sessions, and a panel discussion.
Submission Requirements
We solicit long papers (7 pages) and short papers (4 pages) with unlimited references/appendices. The contributions will be non-archival but will be hosted on our workshop website.
We will also announce a Best Paper Award at our workshop sponsored by Amazon.
Papers must be formatted in AAAI two-column, camera-ready style; see the AAAI-25 author kit for details.
Submission Site Information
OpenReview https://openreview.net/group?id=AAAI.org/2025/Workshop/KnowFM
Organizing Committee
Manling Li (Northwestern University, manling.li@northwestern.edu, primary contact)
Zoey Sha Li (Amazon, slliz@amazon.com)
Mor Geva (Tel Aviv University, Google, morgeva@tauex.tau.ac.il)
Xiaozhi Wang (Tsinghua University, wangxz20@mails.tsinghua.edu.cn)
Chi Han (University of Illinois Urbana-Champaign, chihan3@illinois.edu)
Shangbin Feng (Univ
ersity of Washington, shangbin@cs.washington.edu)
Silin Gao (EPFL, silin.gao@epfl.ch)
Advising Committee
Heng Ji (University of Illinois Urbana-Champaign, hengji@illinois.edu)
Mohit Bansal (University of North Carolina Chapel Hill, mbansal@cs.unc.edu)
Isabelle Augenstein (University of Copenhagen, augenstein@di.ku.dk)
Workshop URL
W49: Workshop on Health Intelligence (W3PHIAI-25)
Integrating information from now widely available -omics and imaging modalities at multiple time and spatial scales with personal health records has become the standard of disease care in modern public health. Moreover, given the ever-increasing role of the World Wide Web as a source of information in many domains, including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. The advances in web science and technology for data management, integration, mining, classification, filtering, and visualization have given rise to various applications representing real-time data on epidemics.
Furthermore, to tackle and overcome several issues in personalized healthcare, the evolution of information technology is crucial. It will improve communication, collaboration, and teamwork among patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. All these changes require novel solutions, and your role as a member of the AI community is pivotal. You are well-positioned to provide both theoretical- and application-based methods and frameworks, making your contribution invaluable.
The workshop will showcase a diverse range of original contributions to theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications. This variety of applications, with a focus on population and personalized health, is a testament to the exciting potential of the field.
Topics
- Knowledge Representation and Extraction
- Integrated Health Information Systems
- Patient Education
- Patient-Focused Workflows
- Shared Decision Making
- Geographical Mapping and Visual Analytics for Health Data
- Social Media Analytics
- Epidemic Intelligence
- Predictive Modeling and Decision Support
- Semantic Web and Web Services
- Biomedical Ontologies, Terminologies, and Standards
- Bayesian Networks and Reasoning under Uncertainty
- Temporal and Spatial Representation and Reasoning
- Case-based Reasoning in Healthcare
- Crowdsourcing and Collective Intelligence
- Risk Assessment, Trust, Ethics, Privacy, and Security
- Sentiment Analysis and Opinion Mining
- Computational Behavioral/Cognitive Modeling
- Health Intervention Design, Modeling and Evaluation
- Online Health Education and E-learning
- Mobile Web Interfaces and Applications
- Applications in Epidemiology and Surveillance (e.g., Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening)
- Hybrid Methods
- Generative AI and Foundation models
- Computational Models of Ageing
This year’s workshop theme is “Application of Generative AI in Medicine and Healthcare.” The workshop will include a welcome session, keynotes, and invited talks, full/short paper presentations, demos, posters, and a special track focused on generative AI.
Format
We invite workshop participants to submit their original contributions through EasyChair following the AAAI format. Three categories of contributions are sought: full-research papers up to 8 pages, short papers up to 4 pages, and posters and demos up to 2 pages.
Submission Information
Submissions to the special track can be either full or short papers, and we ask authors to select the special track option during submission.
Workshop Organizers and Chairs
Martin Michalowski, PhD, FAMIA, FIAHSI (Co-chair), University of Minnesota; Arash Shaban-Nejad, PhD, MPH (Co-chair), The University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics; Simone Bianco, PhD (Co-chair), Altos Labs – Institute of Computation.