The Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24) is a venue for papers describing highly innovative realizations of AI technology. The objective of the conference is to showcase successful applications and novel uses of AI. The conference will use technical papers, best practice papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of AI applications; and to promote an interchange of ideas between basic and applied AI and the discourse on the actual deployment of AI in practice. The general goal of the conference is to teach people the challenges and solutions to accomplishing something useful in the real world, as opposed to describing a new algorithm.

IAAI-24 Schedule

Tracks with Accepted Papers

---

Deployed Highly Innovative Applications of AI​ 

Papers submitted to this track must describe deployed applications with measurable benefits that include an innovative use of AI technology. Applications are defined as deployed once they are in production use by their final end-users and the in-use experience can be meaningfully collected and reported. The study may evaluate either a stand-alone application or a component of a complex system.  

General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants
Kyoung Jun Lee; Baek Jeong; Suhyeon Kim; Dam Kim; Dongju Park

Transformer-Empowered Multi-Modal Item Embedding for Enhanced Image Search in E-commerce
Chang Liu; Peng Hou; Anxiang Zeng; Han Yu

The Virtual Driving Instructor: Multi-Agent System Collaborating via Knowledge Graph for Scalable Driver Education
Johannes Rehm; Irina Reshodko; Stian Zimmermann Børresen; Odd Erik Gundersen

HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation
Hao Sun; Xiaoli Tang; Chengyi Yang; Zhenpeng Yu; Xiuli Wang; Qijie Ding; Zengxiang Li; Han Yu

High Significant Fault Detection in Azure Core Workload Insights
Pranay Lohia; Laurent Boué; Sharath Ranganath; Vijay Agneeswaran

IBCA: An Intelligent Platform for Social Insurance Benefit Qualification Status Assessment
Yuliang Shi; Lin Cheng; Cheng Jiang; Hui Zhang; Guifeng Li; Xiaoli Tang; Han Yu; Zhiqi Shen; Cyril Leung

Some Like It Small: Czech Semantic Embedding Models for Industry Applications
Jiří Bednář; Jakub Náplava; Petra Barančíková; Ondřej Lisický

Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs
Jayachandu Bandlamudi; Kushal Mukherjee; Prerna Agarwal; Ritwik Chaudhuri; Rakesh Pimplikar; Sampath Dechu; Alex Straley; Anbumunee Ponniah; Renuka Sindhgatta

Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
Siva Likitha Valluru; Biplav Srivastava; Sai Teja Paladi; Siwen Yan; Sriraam Natarajan

DCV2I: A Practical Approach for Supporting Geographers’ Visual Interpretation in Dune Segmentation with Deep Vision Models
Anqi Lu; Zifeng Wu; Zheng Jiang; Wei Wang; Eerdun Hasi; Yi Wang

KAMEL: Knowledge Aware Medical Entity Linkage to Automate Health Insurance Claims Processing
Sheng Jie Lui; Cheng Xiang; Shonali Krishnaswamy

Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping
Zainab Akhtar; Umair Qazi; Aya El-Sakka; Rizwan Sadiq; Ferda Ofli; Muhammad Imran

A Submodular Optimization Approach to Accountable Loan Approval
Kyungsik Lee; Hana Yoo; Sumin Shin; Wooyoung Kim; Yeonung Baek; Hyunjin Kang; Jaehyun Kim; Kee-Eung Kim

Check-In Desk Scheduling Optimisation at CDG International Airport
Thibault Falque; Gilles Audemard; Christophe Lecoutre; Bertrand Mazure

Emerging Applications of AI 

Emerging applications papers ‘bridge the gap’ between basic AI research and case studies of deployed AI applications, by discussing efforts to apply AI tools, techniques, or methods to real-world problems in novel ways. 

A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level
J. Fredrik R. Bjørnland; Yrjar Gedde; Johannes Rehm; Irina Reshodko; Odd Erik Gundersen

Tell Me What Is Good about This Property: Leveraging Reviews for Segment-Personalized Image Collection Summarization
Monika Wysoczanska; Moran Beladev; Karen Lastmann Assaraf; Fengjun Wang; Ofri Kleinfeld; Gil Amsalem; Hadas Harush Boke

A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation
Prateeti Mohapatra; Gargi Dasgupta

BERTground: A Transformer-Based Model of Background Spectra on the ISS-Based NICER Space Telescope
Anh N. Nhu; Abderahmen Zoghbi

AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Santosh Palaskar; Vijay Ekambaram; Arindam Jati; Neelamadhav Gantayat; Avirup Saha; Seema Nagar; Nam H. Nguyen; Pankaj Dayama; Renuka Sindhgatta; Prateeti Mohapatra; Harshit Kumar; Jayant Kalagnanam; Nandyala Hemachandra; Narayan Rangaraj

Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors
Bhanu Teja Gullapalli; Stephanie Carreiro; Brittany P Chapman; Eric L Garland; Tauhidur Rahman

Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows
Prerna Agarwal; Harshit Dave; Jayachandu Bandlamudi; Renuka Sindhgatta; Kushal Mukherjee

Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data
Glenn Bruns; Michael Haidar

Interactive Mars Image Content-Based Search with Interpretable Machine Learning
Bhavan Vasu; Steven Lu; Emily Dunkel; Kiri L. Wagstaff; Kevin Grimes; Michael Mcauley

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management
Bingxuan Li; Antonio Castellanos; Pengyi Shi; Amy Ward

Improving Health Information Access in the World’s Largest Maternal Mobile Health Program via Bandit Algorithms
Arshika Lalan; Shresth Verma; Paula Rodriguez Diaz; Panayiotis Danassis; Amrita Mahale; Kumar Madhu Sudan; Aparna Hegde; Milind Tambe; Aparna Taneja

ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations
Muntabir Hasan Choudhury; Lamia Salsabil; William A. Ingram; Edward A. Fox; Jian Wu

Data-Driven Structural Fire Risk Prediction for City Properties
Rupasree Dey; Alan Fern

Attention-Based Models for Snow-Water Equivalent Prediction
Krishu K Thapa; Bhupinderjeet Singh; Supriya Savalkar; Alan Fern; Kirti Rajagopalan; Ananth Kalyanaraman

Redefining the Laparoscopic Spatial Sense: AI-Based Intra- and Postoperative Measurement from Stereoimages
Leopold Müller; Patrick Hemmer; Moritz Queisner; Igor Sauer; Simeon Allmendinger; Johannes Jakubik; Michael Vössing; Niklas Kühl

VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch
Sawinder Kaur; Yi Xiao; Asif Salekin

CHRONOS: A Schema-Based Event Understanding and Prediction System
Maria Chang; Achille Fokoue; Rosario Uceda-Sosa; Parul Awasthy; Ken Barker; Sadhana Kumaravel; Oktie Hassanzadeh; Elton Soares; Tian Gao; Debarun Bhattacharjya; Radu Florian; Salim Roukos

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Harsh Kumar; Tong Li; Jiakai Shi; Ilya Musabirov; Rachel Kornfield; Jonah Meyerhoff; Ananya Bhattacharjee; Chris Karr; Theresa Nguyen; David Mohr; Anna Rafferty; Sofia Villar; Nina Deliu; Joseph Jay Williams

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization
Xi Yang; Rohan R. Arora; Saurabh Jha; Chandra Narayanaswami; Cheuk Lam; Jerrold Leichter; Yu Deng; Daby M. Sow

TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
ChungYi Lin; Shen-Lung Tung; Hung-Ting Su; Winston H. Hsu

Symbol Description Reading
Karol Lynch; Bradley Eck; Joern Ploennigs

Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks
Marc W. Brittain; Luis E. Alvarez; Kara Breeden

Deployed Innovative Tools for Enabling AI Applications 

Within this track, are papers describing *deployed* tools for improving applied AI innovation and deployment of AI systems.

A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning
Neil Thompson; Martin Fleming; Benny J. Tang; Anna M. Pastwa; Nicholas Borge; Brian C. Goehring; Subhro Das

Building Higher-Order Abstractions from the Components of Recommender Systems
Serdar Kadıoğlu; Bernard Kleynhans

End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
Md Nasim; Xinghang Zhang; Anter El-Azab; Yexiang Xue

Innovative Inter-disciplinary AI Integration 

This track is devoted to the integration of AI components with the focus on how the orchestration of methods from different AI silos requires the adaptation of existing technologies to allow them to work together well for application of AI in practice. 

Automated State Estimation for Summarizing the Dynamics of Complex Urban Systems Using Representation Learning
Maira Alvi; Tim French; Philip Keymer; Rachel Cardell-Oliver

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration
Zakaria Mehrab; Logan Stundal; Srinivasan Venkatramanan; Samarth Swarup; Bryan Leroy Lewis; Henning S. Mortveit; Christopher L. Barrett; Abhishek Pandey; Chad R. Wells; Alison P. Galvani; Burton H. Singer; Seyed M. Moghadas; David Leblang; Rita R. Colwell; Madhav V. Marathe

AI Incidents and Best Practices 

Papers in this track analyze the factors related to AI incidents and the best practices for preventing or mitigating their recurrence. 

AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures
Eli Sherman; Ian Eisenberg

When Your AI Becomes a Target: AI Security Incidents and Best Practices
Kathrin Grosse; Lukas Bieringer; Tarek R. Besold; Battista Biggio; Alexandre Alahi

Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database
Christina P. Walker; Daniel S. Schiff; Kaylyn Jackson Schiff

AI Evaluation Authorities: A Case Study Mapping Model Audits to Persistent Standards
Arihant Chadda; Sean McGregor; Jesse Hostetler; Andrea Brennen

IAAI-24 Co-Chairs: 

• Alex Wong (University of Waterloo, Canada) 
• YuHao Chen (University of Waterloo, Canada) 
• Jan R. Seyler (Festo, Germany)