New Faculty Highlights

This year, AAAI is continuing its invited speaker program, highlighting AI researchers who have just begun careers as new faculty members or the equivalent in industry. 

New Faculty Highlight talks will be allotted 30 minutes each; the aim for these talks is to broadly survey the candidate’s research to date. Several talks will be released online and publicized each day of the AAAI conference, following which they will be available archivally as part of the conference program. Invited speakers will be further invited to contribute an article to a corresponding series in AI Magazine. 

Program Schedule

Program Overview

Representation-driven Option Discovery in Reinforcement Learning

Marlos C. Machado

The ability to reason at multiple levels of temporal abstraction is a fundamental aspect of intelligence. In reinforcement learning, this attribute is often modeled through temporally extended courses of actions called options. Despite the popularity of options as a research topic, they are seldom included as an explicit component in traditional solutions within the field. In this talk, I will try to provide an answer for why this is the case and emphasize the vital role options can play in continual learning. Rather than assuming a predetermined set of options, I will introduce a general framework for option discovery, which utilizes the agent’s representation to discover useful options. By leveraging these options to generate a rich stream of experience, the agent can improve its representations and learn more effectively. This representation-driven option discovery approach creates a virtuous cycle of refinement, continuously improving both the representation and options, and it is particularly effective for problems that require agents to exhibit different levels of abstractions to succeed.

Scalable Vision-Language Understanding and Generation

Linchao Zhu

Recent advances in vision-language models have shown remarkable potential, yet creating scalable systems that can effectively understand and generate across modalities remains challenging. This talk will present our contributions to advancing scalable vision-language systems, focusing on three key themes: (1) efficient vision-language understanding, including our work on temporal perceiving video-language pre-training and knowledge-enhanced zero-shot retrieval; (2) scalable generation frameworks, encompassing our innovations in zero-shot captioning and co-speech gesture generation; and (3) practical applications and deployments of these technologies. We will discuss how these advances have enabled both better performance and improved efficiency in real-world scenarios, and explore future directions for scalable multimodal systems.

Bad AI, Good AI – Rethinking the Agency of Our Artificial Teammates

Reuth Mirsky

A prevalent assumption in human-robot and human-AI teaming is that the artificial teammate should be obedient and compliant. In this talk, I challenge the assumption that autonomous agents should always obey instructions and comply with our expectations. I argue that sometimes, to maximize their potential in the team, AI agents should employ intelligent disobedience. I will use guide dogs as inspiration to discuss several exciting manifestations of intelligent disobedience in AI and robotics: reasoning about other agents, initiating an interaction, teaching teammates, and more.

Foundations of Multi-Agent Learning in Dynamic Environments: Where Reinforcement Learning Meets Strategic Decision-Making

Kaiqing Zhang

Recent years have witnessed tremendous successes of learning for sequential decision-making, and in particular, Reinforcement Learning (RL). Prominent application examples include playing Go and video games, robotics, autonomous driving, and recently large language models. Most such success stories naturally involve multiple agents. Hence, there has been surging research interest in advancing Multi-Agent Learning in Dynamic Environments, and particularly, multi-agent RL (MARL), to which my research has led and made significant contributions. In this talk, I will review my works on this topic, by discussing the near-optimal sample complexity of model-based multi-agent RL in two-player stochastic games, the fundamental computational hardness of RL in general-sum stochastic games, as well asl independent learning in stochastic games, which provides a more Economics perspective for MARL. Time permitting, I will also discuss our works on the applications on robot fleet learning, and recent results on multi-agent learning with large-language-model agents.

Knowledge-driven Scientific Large Language Models

Qiang Zhang

Scientific research has been revolutionized by artificial intelligence, with numerous successes in biology and chemistry. In this talk, I will focus on the interdisciplinary field of AI driven biological and chemical discovery, aiming to establish language and knowledge driven machine learning models. By leveraging the capable language model and symbolic domain knowledge, our team has published a number of AI models that achieve promising results in tasks such as protein and molecular function prediction, and design/generation. The published models serve as a bridge between human and nature, helping humans better understand and explore the natural world.

Towards Robust, Efficient and Practical Decision Making: From Reward-Maximizing Deep Reinforcement Learning to Reward-Matching GFlowNets

Ling Pan

Recent years have witnessed the great success of reinforcement learning with deep feature representations in many challenging tasks, including LLMs, computer games, robotics, and so on. Yet, solely focusing on the optimal solution based on a reward proxy and learning the reward-maximizing policy is not enough. Diversity of the generated states is desirable in a wide range of important practical scenarios such as dialogue systems, drug discovery, recommender systems, etc. For example, in molecule generation, the reward function used in in-silico simulations can be uncertain and imperfect itself (compared to the more expensive in-vivo experiments). Therefore, it is not sufficient to only search for the solution that maximizes the return. Instead, it is desired that we sample many high-reward candidates, which can be achieved by sampling them proportionally to the reward of each terminal state. The Generative Flow Networks (GFlowNets) is a probabilistic generative model where an agent learns a stochastic policy for object generation, such that the probability of generating an object is proportional to a given reward function, i.e., by learning a reward-matching policy. Its effectiveness has been shown in discovering high-quality and diverse solutions in molecule generation, biological sequence design, alignment of foundation models, etc. The talk concerns my recent research works about how we tackle three important challenges in such decision-making systems. Firstly, how can we ensure a robust learning behavior and value estimation of the agent? Secondly, how can we improve its learning efficiency? Thirdly, how to successfully apply them in important practical applications such as computational sustainability problems and combinatorial optimization?

Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

Aryan Deshwal

A huge range of scientific discovery and engineering design problems ranging from materials discovery and drug design to 3D printing and chip design can be formulated as the following general problem: adaptive optimization of complex design spaces guided by expensive experiments where expense is measured in terms of resources consumed by the experiments. For example, searching the space of materials for a desired property while minimizing the total resource-cost of physical lab experiments for their evaluation. The key challenge is how to select the sequence of experiments to uncover high-quality solutions for a given resource budget. In this talk, I will introduce novel adaptive experiment design algorithms to tackle this challenge. This includes both designing new predictive models over combinatorial objects (sequences, graphs, permutations, etc.) that work well in small data settings and provide principled uncertainty quantification and decision policies to carefully select each experiment for complex real-world settings where there are usually multiple objectives, multi-fidelity experiments, and black-box constraints. I will also present results on applying these algorithms to solve high-impact science and engineering applications in domains including nanoporous materials discovery, electronic design automation, and additive manufacturing. In the end, I will also cover some open challenges and future directions that I am excited about.

Certified Trustworthiness in the Era of Large Language Models

Linyi Li

Along with the wide deployment of deep learning (DL) systems, their lack of trustworthiness, such as their lack of robustness, fairness, and numerical reliability, is raising serious social concerns, especially in safety-critical scenarios such as autonomous driving and aircraft navigation. Hence, a rigorous and accurate evaluation of the trustworthiness of DL systems is important and would be a prerequisite for improving DL trustworthiness. The first part of the talk will be an overview of certified methods for DL trustworthiness. These methods provide computable guarantees for DL systems in terms of worst-case trustworthiness under certain realistic conditions, such as the accuracy lower bound against arbitrary tiny perturbations. Based on our taxonomy and systematization, we reveal key methodologies and implications of certified DL trustworthiness. We will illustrate two concrete methods: semantic randomized smoothing and branch-and-bound with differentiable linear relaxations.

As a representative of recent DL breakthroughs, large language models (LLMs) are transforming our lives, but on the other hand, posing more challenges to trustworthiness. For example, LLMs can be jailbroken with adversarial prompts to output harmful content with bias, harassment, misinformation, and more. The second part of the talk will be an overview of LLM trustworthiness. We will start with sharing my hands-on experience in developing state-of-the-art LLMs in the industry, then illustrate common LLM trustworthiness issues via examples, and conclude by envisioning certifiable trustworthiness for LLMs.

Robots Learning Through Physical Interaction and Intelligence

Roberto Martin-Martin

Artificial Intelligence (AI) has advanced significantly in fields like computer vision (CV) and natural language processing (NLP), fueled by breakthroughs in machine learn-
ing. However, these strides have yet to fully extend to robotics, which faces unique challenges in long-horizon decision-making. Predicting and utilizing physical interactions to alter the environment remains complex for artificial agents. Consequently, robots minimize physical interaction to avoid risks in uncontrolled environments, relying heavily on computation for perception, planning, and control. In contrast, humans demonstrate that autonomy and intelligent behavior arise from exploiting rich interactions with the environment, reducing reliance on precise computation. My research aims to balance interaction and computation in robot learning systems that reason, perceive, and plan within the interaction loop, emphasizing physical interaction as essential in unstructured environments. To that end, my work spans three complementary research directions: imitation learning for Physical Interactive Intelligence, Physical Interactive Intelligence in navigation, and perceiving through Physical Interactions. I will also elaborate on my future steps towards “Ever Improving Physically Interacting Robots”.

Multisensory Machine Intelligence

Ruohan Gao 

The future of Artificial Intelligence demands a paradigm shift towards multisensory perception—to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision making. While we humans perceive the world by looking, listening, touching, smelling, and tasting, traditional form of machine intelligence mostly focuses on a single sensory modality, particularly vision. Therefore, my research, which I call multisensory machine intelligence, aims to empower machines to emulate and enhance human capabilities in seeing, hearing, and feeling, ultimately enabling them to comprehensively perceive, understand, and interact with the multisensory world. In my talk, I will present my research that studies two important aspects of the multisensory world: 1) multisensory objects, and 2) multisensory space. In both aspects, I will talk about how we design systems to reliably capture multisensory data from real-world objects and space, how we effectively model them with differentiable simulation algorithms that build a unified multisensory representation to virtualize real objects, and how we explore creative cross-modal/multi-modal applications with sight, sound, and touch in vision, graphics, and robotics. In the end, I will briefly conclude with my future plans.

Efficient Robot Learning via Interaction with Humans

Erdem Bıyık

In many human-robot collaboration and multi-agent tasks, it is vital to model the partners and estimate their objectives to efficiently collaborate/interact with them. While learning from demonstrations is the most common approach for this, it is very data-hungry, which we cannot afford in many settings including robotics, and demonstrations are unreliable in a surprisingly large number of domains, including those we think humans perform reasonably well, e.g., driving. In this talk, I will start with introducing comparison-based feedback and explain why it does not suffer from most of the problems that demonstrations have, but is still data-hungry. To address this problem, I will propose comparative language based feedback and active learning techniques, which will result in (1) a new type of human feedback, and (2) an active querying algorithm that optimizes the information the AI agent will elicit from the human. I will conclude the talk by discussing what other types of human feedback exist, e.g., interventions or hand gestures, and how we can incorporate them into the existing learning algorithms.

Scalable, Sustainable, Generalizable, and Responsible AI for Public Sector

Ryan Shi

AI for public sector research is about using AI to tackle the numerous challenges faced by public sector organizations when they are out there making our world a better place. AI for public sector research is use-inspired research. It differs from traditional AI research first and foremost in its key objective being measurable societal impact. AI for public sector research contributes to the computing community by proposing new problem models, raising complexities that challenge abstractions which often leads to new methodologies, and introducing new contexts for evaluation. However, fulfilling this promise is easier said than done. This talk consists of three parts about our preliminary work in our long-term quest to make AI for public sector operationally scalable, financially sustainable, technically generalizable, and socially responsible. We will cover (1) a concrete AI for public sector project from problem scoping to field trials and deployment, (2) a generalizable algorithm applicable to various public sector domains, and (3) an overview of our work in a wide variety of applications.

Scientific Machine Learning in the New Era of AI: Foundations, Visualization, and Reasoning

Wuyang Chen

The rapid advancements in artificial intelligence (AI), propelled by data-centric scaling laws, have significantly transformed our understanding and generation of both vision and language. However, natural media, such as images, videos, and languages, represent only a fraction of the modalities we encounter, leaving much of the physical world underexplored. We propose that Scientific Machine Learning (SciML) offers a knowledge-driven framework that complements data-driven AI, enabling us to better understand, visualize, and interact with the diverse complexities of the physical world.
In this talk, we will delve into the cutting-edge intersection of AI and SciML. First, we will discuss the automation of scientific analysis through multi-step reasoning grounded with formal languages, paving the way for more advanced control and interactions in scientific models. Second, we will demonstrate how SciML can streamline the visualization of intricate geometries, while also showing how spatial intelligence can be adapted for more robust SciML modeling. Finally, we will explore how scaling scientific data can train foundation models that integrate multiphysics knowledge, thereby enhancing traditional simulations with a deeper understanding of physical principles.

Representation Learning: A Causal Perspective

Yixin Wang

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions captures. However, these desiderata are often intuitively defined and challenging to quantify or enforce.
In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning.
This is joint work with Michael Jordan, Kartik Ahuja, Divyat Mahajan, and Yoshua Bengio:
[1] https://arxiv.org/abs/2109.03795
[2] https://arxiv.org/abs/2209.11924

Efficient and Trustworthy Learning: Formulation and Optimization

Tian Li

Machine learning has revolutionized tremendous real-world applications. However, its deployment remains hindered by challenges of scalability and trustworthiness (fairness, robustness, and privacy). My work aims to develop principled problem formulation and optimization algorithms for efficient and trustworthy learning. In this talk, I first introduce objectives that rigorously integrate fairness and robustness constraints. I then discuss distributed optimization algorithms that are particularly designed for practical settings (i.e., with heterogeneous data sources, resource constraints, privacy requirements, or the latest model architectures). I will also explore the interconnections between these research directions.

Towards Robust, Efficient and Practical Decision Making: From Reward-MaximizingMultimodal Understanding and Generation with Efficiently Finetuned Foundation Models Deep Reinforcement Learning to Reward-Matching GFlowNets

Long Chen

Today’s pretrained foundation models have demonstrated astonishing abilities in different applications. Hundreds of foundation models have been proposed during the past few years. Although significant progress has been achieved, a notorious weakness for finetuning foundation models is the high training cost. In this talk, I am going to share a series of work on efficiently finetuning foundation models, including parameter-efficient, memory-efficient, modality-efficient, and annotation-efficient training. Meanwhile, I will also share some typical downstream multimodal understanding and generation applications with these efficiently finetuned foundation models.

Transforming Healthcare Decision Making Using Artificial Intelligence

Shengpu Tang

Decision making is at the core of healthcare: clinicians constantly make complex decisions that span diagnosis, treatment, care coordination, and resource allocation. Yet, human decisions are never perfect, leading to suboptimal patient care. My research uses AI to augment and improve decision-making in healthcare, following a synergistic approach that combines novel AI methods with practical, real-world implementation. Here, I will explore two key themes: Application-Inspired AI Innovations, focused on novel AI methods grounded in practical healthcare problems; and Path to Deployment and Impact, which addresses AI integration into clinical workflows for real-world improvements.

Persuasion for Social Good: How to Build and Break AI

Jiaqi Ma

Persuasion is important in numerous situations like healthy habit promotion, and emotional support. As AI gets more involved in our daily life, it becomes critical to study how they can persuade humans and how persuasive they are. In this talk, I will cover (1) how to build such persuasive AI systems that can persuade, negotiate, and cooperate with other humans in the game of Diplomacy. (2) I will also discuss how humans perceive such specialized AI systems. This study validates the necessity of California’s Autobot Law and proposes guidance to regulate such systems. (3) As these systems become more powerful, AI safety problems become more important. So I will describe how to persuade AI models to jailbreak them and study AI safety problems. Finally, I will conclude with my long-term vision to further study persuasion from a multi-angle approach that combines Artificial Intelligence, Human-Computer Interaction, and social sciences.

AI Governance and Lessons Learned as an AI Policy Advisor in the United States Senate

Serena Booth

Prior to assuming my role as an Assistant Professor of Computer Science at Brown University, I served as an AI Policy Advisor through a AAAS Fellowship in the U.S.~Senate. I worked for the Committee on Banking, Housing, and Urban Affairs on the Democrat side. In this unconventional talk, I will detail the lessons I learned about legislating, regulating, and governing AI systems as a policymaker, and how these lessons influence my research agenda as a new PI.

Breaking the Resource Monopoly from Industries: Sustainable and Reliable LLM Serving By Recycling Outdated and Resource-Constrained GPUs

Tianlong Chen

In recent years, Large Language Model (LLM) agents, exemplified by models like ChatGPT, and PaLM, have showcased remarkable prowess in various tasks, owing to their vast number of parameters and emergent in-context learning capabilities. To serve these gigantic models with billions of parameters, it is a trend and becomes a must to explore how to use the existing hardware, especially outdated hardware, to collectively improve environmental sustainability, efficiency, and reliability for LLM serving. A few pioneering examples include Microsoft’s Project Natick, Google’s TPU Pod Optimization, Alibaba’s Cloud Server Repurposing, and Facebook’s Network Hardware Reuse. In this talk, I will traverse my series of contributions with promising new directions, particularly emphasizing modularized LLM architecture (Part 1), in-storage sustainable computing (Part 2), and reliable serving against software and hardware attacks (Part 3).

Compression-Aware Computing for Scalable and Sustainable AI

Zhaozhuo Xu

This talk explores the challenge of customizing large-scale AI models, particularly generative AI, on cost-effective devices with limited memory and energy resources. Modern AI models demand substantial computational power, often relying on specialized hardware such as GPUs. To address this, the talk introduces compression-aware computing, a framework enabling AI models to recognize and adapt to their compressed states while preserving performance. Compression-aware computing integrates compression techniques like sparsification, quantization, and low-rank decomposition to enhance the efficiency and accuracy of AI models, broadening these models’ accessibility across diverse devices. Additionally, this talk highlights one rationale of scalable and sustainable AI in advancing Alzheimer’s research by facilitating the analysis of large single-cell transcriptomics datasets for gene-gene interaction discovery.

Learning Language Structures through Grounding

Freda Shi

Syntactic and semantic structures model the language processing behaviors of humans, and can serve as the foundation for machine learning–based natural language processing (NLP) models. However, such structures can be expensive to annotate, rendering the conventional supervised training approach inefficient or even infeasible in a number of scenarios.
In this talk, I will present my work on learning both syntactic and semantic structures of language through various grounding signals that naturally exist in the world. I will start by introducing the task of visually grounded grammar induction and our proposed solution based on visual concreteness estimation. Second, I will describe our work on learning the semantic structures of sentences (i.e., their corresponding programs) by grounding program implementations to their execution outcomes. Towards language-universal NLP, I will describe our work that efficiently transfers models from one language to another without any human annotations in the target language, using cross-lingual word correspondence as grounding signals. I will conclude with a discussion on the future directions of this line of work.

Axioms for AI Alignment from Human Feedback

Evi Micha

In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generalizations fail to meet basic axioms. In response, we develop novel rules for learning reward functions with strong axiomatic guarantees. A key innovation from the standpoint of social choice is that our problem has a linear structure, which greatly restricts the space of feasible rules and leads to a new paradigm that we call linear social choice.

Scaling up Reinforcement Learning

Aviral Kumar

Reinforcement learning (RL) offers a powerful framework for learning behavioral strategies or policies from data and experience, often surpassing supervised and unsupervised learning approaches in its ability to discover novel and superior behaviors. However, most RL advances in the last decade have largely been made in settings with limited data, small-scale models, and domain-specificity. In this talk, I will present my research on scaling RL to overcome these barriers, unlocking new capabilities and efficiencies.
In the first part of the talk, I will talk about offline RL, a paradigm that aims to address the data problem by learning policies from previously-collected experience, without any additional environment interaction. I will introduce an algorithm (conservative Q-learning) and present its favorable scaling results with data quality and model size. I will then discuss how to combine offline RL with the conventional RL paradigm of learning from active interaction (i.e., online RL), resulting in “hybrid” algorithms that enjoy the best of learning from diverse historical data as well as interaction. 
In the second part, I will talk about how techniques from offline RL, when scaled up appropriately, can be used to improve reasoning and agentic capabilities of generalist foundation models such as LLMs, often entirely with autonomously-collected suboptimal data. Our methods often lead to drastic improvements: about 6-8x in compute efficiency, 8x in data efficiency, and are the first ones to unlock new capabilities with the use of more inference-time computation. 
I will conclude my talk with a brief discussion of some future directions at the intersection of RL and scaling, that I believe are exciting, challenging, and impactful.

Mitigating Bias in Machine Learning: A Comprehensive Review and Novel Approaches

Mahdi Khalili

Machine learning algorithms are increasingly used in our daily lives, yet often exhibit discrimination against protected groups. In this talk, I discuss the growing concern of bias in ML and overview existing approaches to address fairness issues. Then, I present three novel approaches developed by my research group. The first leverages generative AI to eliminate biases in training datasets, the second tackles non-convex problems arise in fair learning, and the third introduces a matrix decomposition-based post-processing approach to identify and eliminate unfair model components while preserving accuracy.

Beyond Accuracy: Building Reliable, Cost-Effective Sparse Models

Dongkuan Xu

In an era where AI models are scaling to unprecedented sizes, accuracy alone is no longer enough. This talk dives into the challenges and innovations of sparse training as a solution to the soaring computational demands of deep neural networks. Sparse models offer a way to drastically cut down memory, energy, and computational costs, but these benefits often come at the expense of reliability—especially in real-world, safety-critical applications. We explore the latest advances in sparse training techniques designed to enhance model reliability without sacrificing efficiency. By examining innovative methods such as structured and unstructured sparse masking, weight averaging, and unknown-aware loss functions, we demonstrate how sparse models can achieve both high performance and calibrated confidence. From model pruning to dynamic mask adjustments, this talk introduces a comprehensive framework for optimizing sparse models, including theoretical insights and experimental results across various datasets. Join us as we push beyond the accuracy paradigm and redefine what it means to build scalable, dependable AI in resource-constrained environments.

Realizing AI for Impact: Towards Participatory Human-AI Collaboration for Water Conservation and Reproductive Health

Elizabeth Bondi-Kelly

AI has immense potential for positive social impact, including in domains ranging from conservation to health. However, it can be challenging to account for human collaborations and real-world uncertainties when deploying such systems, which can lead to critical errors. Therefore, my research focuses on developing new methods in multi-agent systems and machine learning, including methods for participatory design of AI, human-AI collaboration, and uncertainty quantification, to develop safe, impactful AI systems, particularly in the domains of water conservation and reproductive health.

Towards Trustworthy Machine Learning under Distribution Shifts

Jun Wu

Transfer learning refers as to the transfer of knowledge or information from a source domain to a relevant target domain. There are two key challenges: distribution shifts and trustworthiness concerns. To address these challenges, my research aims to understand transfer learning from the perspective of knowledge transferability (e.g., IID and non-IID learning tasks) and trustworthiness (e.g., adversarial robustness, data privacy, and performance fairness).

Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems

Daniel Brown

Ensuring that AI systems do what we, as humans, actually want them to do, is one of the biggest open research challenges in AI alignment and safety. My research seeks to directly address this challenge by enabling AI systems to interact with humans to learn aligned and robust behaviors. The way robots and other AI systems behave is often the result of optimizing a reward function. However, manually designing good reward functions is highly challenging and error prone, even for domain experts. Although reward functions for complex tasks are difficult to manually specify, human feedback in the form of demonstrations or preferences is often much easier to obtain. However, human data is often difficult to interpret due to ambiguity and noise. Thus, it is critical that AI systems take into account uncertainty over the human’s true intent. My talk will give an overview of my lab’s progress along the following fundamental research areas: (1) efficiently maintaining uncertainty over human intent, (2) directly optimizing behavior to be robust to uncertainty over human intent, and (3) actively querying for additional human input to reduce uncertainty over human intent.

Harnessing Robust Statistics for Trustworthy AI

Xiaorui Liu

Machine learning techniques are notably vulnerable to natural and adversarial perturbations. These vulnerabilities can lead to potential catastrophic failures with significant economic, ethical, and societal risks. In this talk, I will present my research on harnessing robust statistics to build robust and trustworthy AI systems. Specifically, I will highlight my research breakthroughs in graph learning (GNNs), large language models (LLMs), deep equilibrium models (DEQs), and deep representation learning. These breakthroughs stem from a unified and principled robust statistics framework that incorporates robustness as the core inductive bias in deep learning architecture. This innovative approach has enabled significant improvements in intrinsic robustness and generalization even in complex and challenging environments, which demonstrates the transformative potential of harnessing robust statistics to enhance the robustness and trustworthiness of AI systems.

Breaking the Resource Monopoly from Industries: Sustainable and Reliable LLM Serving By Recycling Outdated and Resource-Constrained GPUs

Tianlong Chen

In recent years, Large Language Model (LLM) agents, exemplified by models like ChatGPT, and PaLM, have showcased remarkable prowess in various tasks, owing to their vast number of parameters and emergent in-context learning capabilities. To serve these gigantic models with billions of parameters, it is a trend and becomes a must to explore how to use the existing hardware, especially outdated hardware, to collectively improve environmental sustainability, efficiency, and reliability for LLM serving. A few pioneering examples include Microsoft’s Project Natick, Google’s TPU Pod Optimization, Alibaba’s Cloud Server Repurposing, and Facebook’s Network Hardware Reuse. In this talk, I will traverse my series of contributions with promising new directions, particularly emphasizing modularized LLM architecture (Part 1), in-storage sustainable computing (Part 2), and reliable serving against software and hardware attacks (Part 3).

For More Information 

Inquiries concerning submissions and suggestions for the new faculty highlight program may be directed to the program co-chairs at aaai25fhchairs@aaai.org. All other inquiries should be directed to AAAI at aaai25@aaai.org. 

New Faculty Highlight Co-Chairs 

Bistra Dilkina, University of Southern California, USA
Jana Doppa, Washington State University, USA