| Total: 35
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.
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.
This talk examines the intersection of artificial intelligence and policymaking, focusing on legislative and regulatory frameworks in the United States. It explores the role of key federal agencies, existing technology-agnostic laws affecting AI, and gaps in regulatory oversight that require legislative intervention. Consumer protection laws are analyzed for their relevance to AI governance, particularly in financial services. The discussion also highlights the implications for AI research, emphasizing the importance of interdisciplinary collaboration between computer scientists and policymakers to ensure responsible AI development that aligns with democratic values and societal interests.
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 in which 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. Consider trying to write down a reward function that describes good driving behavior or how you like your bed made in the morning. While reward functions for these tasks are difficult to manually specify, human feedback in the form of demonstrations or preferences are 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 epistemic 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.
The ability to reason at multiple levels of temporal abstraction is a fundamental aspect of intelligence. In reinforcement learning (RL), this attribute is often modelled through temporally extended courses of actions called options. In this talk, I will introduce a general framework for option discovery, which uses 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 where agents need to operate at varying levels of abstraction to succeed.
With the astonishing ability of different pretrained foundation models (e.g., large language models (LLMs), vision-language models, diffusion models), today’s AI research and development tendency has been revolutionized. In this talk, I will answer two questions: Q1: How can we efficiently train or fine-tune foundation models? Q2: How can we build strong open-world multimodal understanding and generation models with these pretrained foundation models?
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. People expect the wide usage of LLM serving at edge hardware, personal devices, and organization/enterprise IT infrastructures to revolutionize global access to information, communication, automation, and creativity. However, due to the extreme large-scale LLM parameters (LLaMA 3.1 contains 405 billion of 2 or 4 bytes floating point numbers), the LLM serving is facing significant sustainability pressure due to its requirements on the latest high-embodied carbon hardware (e.g., GPUs, HBMs, memory, storage, and network hardware) and the high operational carbon emissions, leading to a significant and alarming increase in carbon emissions and a high barrier to their widespread deployments and practical applications in various scenarios. Companies, organizations, and institutes usually have the complete general-purpose IT infrastructure, which consists of a large amount of computing, memory, storage, and network hardware. Although these general-purpose IT infrastructures are far more than enough for existing application executions, deploying and executing the LLM for a broad spectrum of serving platforms can be challenging and difficult due to resource limitations. Purchasing the latest hardware including GPUs (e.g., Nvidia H100 or H200) will lead to considerable issues including 1) serious embodied carbon emissions during the new hardware production, 2) no explicitly lower operational carbon emissions with essential modeling and optimizations, 3) high economic and financial pressures, and 4) potentially tremendous existing hardware resource wasting. Therefore, it is a trend and becomes a must to explore how to use the existing hardware, especially outdated hardware, to collectively improve both 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).
Although Artificial Intelligence (AI) has transformed vision and language modeling, Scientific Machine Learning (SciML) complements data-driven AI via a knowledge-driven approach, enhancing our understanding of the physical world. My work focuses on: 1) automating scientific reasoning with language models, 2) improving geometric interpretation, 3) developing foundation models for multiphysics.
Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.
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 AAAI-25 new faculty highlight 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.
Artificial intelligence has made remarkable progress in reasoning over complex, structured, multimodal, and multilingual data, addressing critical challenges in domains such as finance and healthcare. This abstract underscores key advancements in tabular reasoning, temporal analysis, and structured multimodal reasoning. Key contributions include the development of TempTabQA, a benchmark for temporal question answering, along with novel methods for enhancing temporal reasoning in large language models (LLMs). Additionally, a framework for evaluating mathematical reasoning in financial documents has been introduced, establishing robust techniques for interpreting time-sensitive and quantitative data. Building on these foundations, we have developed hybrid SQL-text adaptive reasoning models (H-STAR) and knowledge-aware reasoning techniques for semi-structured tables (MMTabQA), enabling precise and efficient handling of complex queries. In the vision-language domain, our contributions include advancements in spatial reasoning for geographic data (MAPWise), methods to improve robustness in chart interpretation (FlowVQA), and evaluations of LLMs’ ability to understand visual data, such as charts. Furthermore, we have addressed challenges in multilingual and cross-modal robustness through innovations such as multilingual table synchronization (InfoSync), concurrent robustness evaluations across languages and modalities, and numerical reasoning in tabular data. Our work aims to enhance reasoning on dynamically evolving data using hybrid LLM-SQL queries, symbolic query generation, and multi-table retrieval techniques. We also plan to tackle challenges in interpreting hierarchical table structures, analyzing multiple complex chart types, and exploring diverse map types, while advancing real-world multimodal data analysis. Additionally, we plan to improve table generation in both closed/open-book scenarios and refine evaluation frameworks for structured tasks. These advancements demonstrate the potential of AI in tackling complex, multimodal data and delivering impactful real-world solutions.
Machine Learning (ML) 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.
Along with the broad 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 essential 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 illustrate key methodologies, specifically semantic randomized smoothing and branch-and-bound, and their implications for certified DL trustworthiness. 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 hands-on experience in developing fontier LLMs, then illustrate common LLM trustworthiness issues via examples, then demonstrate evaluation challenges, take one benchmark as an example, and conclude by envisioning certifiable trustworthiness for LLMs.
While Large Language Models excel in language processing, Large Agent Models are designed to interact with the environment. This transition poses significant challenges in understanding lower-level visual details, and long-horizon reasoning for effective goal interpretation and decision-making. Despite the impressive performance of LLMs/VLMs on various benchmarks, these models perceive images as bags of words (semantic concepts). In detail, they use semantic understanding as a shortcut but lack the ability to recognize geometric structures or solve spatial problems such as mazes. To interact with the physical world, we focus on two dimensions: (1) From high-level semantic to low-level geometric understanding: We introduce a low-level visual description language that serves as geometric tokens, allowing the abstraction of multimodal low-level geometric structures. (2) From fast-thinking to slow-thinking: We propose to quantify long-horizon reasoning by incorporating Markov Decision Process (MDP) based decision-making. The key difference between language models and agent models lies in their decision-making capabilities. This fundamental difference necessitates a shift in how we approach the development of large agent models, focusing on both geometric understanding and long-term planning to create more capable embodied AI agents.
To build a responsible data economy and protect data ownerhip, it is crucial to enable learning models from separate, heterogeneous data sources without centralization. For example, federated learning (FL) aims to train models across massive remote devices or isolated organizations, while keeping user data local. However, federated learning can face critical practical issues such as scalability, noisy samples, biased learning systems or procedures, and privacy leakage. At the intersection between optimization, trustworthy (fair, robust, and private) ML, and learning in heterogeneous environments, my research aims to support scalable and responsible data sharing to collectively build intelligent models.
The development of large language models has demonstrated robust performance on English-centric benchmarks, which predominantly reflect majority opinions and dominant cultural norms. However, successful deployment in real-world applications requires the ability to handle context-specific and diverse knowledge, which is often underrepresented in training data. Addressing a plurality of perspectives is therefore essential. My research focuses on developing pluralistic evaluation methods to assess the diversity of LLM outputs, with a particular focus on culturally rich common-sense reasoning. Additionally, I work on advancing models that integrate diverse knowledge into LLMs, aiming to bridge the gap between human and AI understanding through the incorporation of varied perspectives using innovative probabilistic frameworks. In this talk, I will emphasize two key directions of my previous work: the probabilistic box model for representing diverse knowledge and probabilistic evaluation for assessing diversity in LLMs, with a focus on distributional aspects. Additionally, I will discuss my efforts to understand model behavior in long-tail scenarios.
Humans have a strong intuitive understanding of the physical world. Through observations and interactions with the environment, we build mental models that predict how the world would change if we applied a specific action (i.e., intuitive physics). My research draws on these human insights to develop model-based RL agents that learn from their interactions and build predictive models that generalize widely across a range of objects made with different materials. The core idea behind my research is to introduce novel representations and integrate structural priors into learning systems to model dynamics at different levels of abstraction. I will discuss how such structures can make model-based planning algorithms more effective, helping robots accomplish complex manipulation tasks (e.g., manipulating an object pile, shaping deformable foam into a target configuration, and making a dumpling from dough using various tools).
Machine learning techniques are notably vulnerable to natural or adversarial perturbations, which can lead to catastrophic failures with significant economic, ethical, and societal risks. In this New Faculty Highlight Talk, I will showcase 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 general 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 approach has enabled significant improvements in intrinsic robustness and generalization, even in complex and challenging environments. My research demonstrates the transformative potential of harnessing robust statistics in enhancing the robustness and trustworthiness of AI systems. Looking forward, I will continue to push this frontier by advocating the design of robustness-informed neural networks across various areas.
Data plays an increasingly crucial role in both the performance and the safety of AI models. Data attribution is an emerging family of techniques aimed at quantifying the impact of individual training data points on a model trained on them, which has found data-centric applications such as instance-based explanation, unsafe training data detection, and copyright compensation. In this talk, I will comprehensively review our work contributing to the applications, methods, and open-source benchmarks of data attribution, and discuss open challenges in this field.
Artificial Intelligence (AI) has revolutionized fields like computer vision and natural language processing, yet its impact on robotics remains limited by challenges in long-horizon decision-making and complex physical interactions. My research pioneers robot learning algorithms that exploit (predict, perceive, plan, and reason about) physical interaction as a core component of artificial intelligence, pushing beyond passive solutions in domains such as perception, navigation, and manipulation. By leveraging techniques in imitation learning and hierarchical reinforcement learning, my work empowers robots to learn from human demonstrations, navigate interactively in real-world environments, and gather information through purposeful interactions. In my talk, I will explain how these advances are critical for robots to become useful helpers in human environments, opening the door to the next generation of household robots. I will present several AI algorithmic innovations to integrate physical interactions in computation procedures and outline the path toward developing continually learning robots capable of operating autonomously in unstructured human environments, enhancing their utility as adaptable and intelligent assistants.
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.
A prevalent assumption in human-robot and human-AI teaming is that artificial teammates should be compliant and obedient. In this talk, I will question this assumption by presenting the Guide Robot Grand Challenge and discussing the components required to design and build a service robot that can intelligently disobey. This challenge encompasses a variety of research problems, as I will exemplify via three challenges: reasoning about the goals of other agents, choosing when to interrupt, and interacting in a tightly coupled physical environment.
In this talk, I will present our recent advances in sequential decision-making systems in reward-maximizing deep RL and the emerging reward-matching GFlowNets. The presentation will examine three fundamental challenges: efficiency, robustness, and practical applications.
Language is highly structured, with syntactic and semantic structures, to some extent, agreed upon by speakers. With implicit or explicit awareness of such structures, humans can learn and use language efficiently and generalize to sentences that contain unseen words. Motivated by human language learning, in this presentation, I will introduce a family of machine learning tasks that learns language structures through grounding, where distant supervision from other data sources (i.e., grounds), including but not limited to different modalities (e.g., vision), execution results of programs, and other languages, are used to guide the learning of language structures. I will demonstrate the potential of this task formulation, advocate for its adoption through three schemes, and discuss the possibility of the general language learning problem through grounding.
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.