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In classical auction design, we take it for granted that the auctioneer is trusted and always implements the auction's rules honestly. This assumption, however, no longer holds in modern auctions based on blockchains, or those mediated by third-party platforms such as Google. For example, in blockchain-based auctions, the consensus nodes that partly act as the […] |
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Abstract: The efficient sharing of AI infrastructures is becoming increasingly important in both public and private data centers. This demand is driven by two key factors: the proliferation of specialized AI models tailored for different users and applications, and the highly dynamic nature of requests, which are often on-demand. Dedicated GPU allocation in such scenarios […] |
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Scalable and efficient machine learning (ML) systems have been instrumental in fueling recent advancements in ML capabilities. However, further scaling these systems requires more than simply increasing the number and performance of accelerators. This is because modern ML deployments rely on complex pipelines composed of many diverse and interconnected systems. In this talk, I will […] |
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Abstract: Modern machine learning models—such as LLMs and recommender systems—interact with humans, companies, and other models in a broader ecosystem. However, these multi-agent interactions often induce unintended ecosystem-level outcomes such as clickbait in classical content recommendation ecosystems, and more recently, safety violations and market concentration in nascent LLM ecosystems. In this talk, I discuss my […] |
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Over the past decade, networked systems have consolidated under just a handful of hyperscale cloud providers (e.g., AWS, Azure). While this offers logistical and economic advantages, attackers specifically target providers and their customers, a shift that has left traditional network vantage points blind to the most sophisticated adversaries. In this talk, I’ll explore how we […] |
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Abstract: Large Language Models (LMs) have demonstrated remarkable capabilities by scaling up training data and model sizes. However, they continue to face critical challenges, including hallucinations and outdated knowledge, which particularly limit their reliability in expert domains such as scientific research and software development. In this talk, I will urge the necessity of moving beyond […] |
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Deep learning's success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization enables this learning process. First, I will show how optimization algorithms exhibit surprisingly rich dynamics when training neural networks, and how these complex dynamics are […]
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Neural language models have opened a fascinating, flexible platform for reasoning in mathematics, programming, and beyond. This talk will explore the intersection of these models and the rigor of formal reasoning. First, I discuss my work on building foundation models for mathematics and using language to guide the search for formally verified proofs. Then, I […] |
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The PCP theorem, proved in the 90’s, shows how to encode a proof for any theorem into a format where the theorem's correctness can be verified by making only a constant number of queries to the proof. This result is a significant milestone in computer science and has important implications for approximation algorithms, cryptography, and […] |
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Abstract: Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However, due to their scale and complexity, we lack a fundamental scientific understanding of much of LLMs’ behavior, even when they are open source. The “black-box” nature of LMs not only complicates model debugging and evaluation, but also limits trust […] |
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As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning settings, which spans both theory and practice and dates back decades, has been the failure of convergence for iterative algorithms such as gradient descent. Accordingly, […]
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In this talk, I will explain how fundamental problems in computational learning theory are at the heart of modern problems in machine learning and scientific applications and how algorithmic insights in mathematically tractable models can inspire new solutions in a wide variety of domains. I will explore two directions. First, I will explore algorithmic foundations […] |
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Team theory is a mathematical formalism for decentralized stochastic control problems in which a “team,” consisting of a number of members, cooperates to achieve a common objective. It was developed to provide a rigorous mathematical framework of cooperating members in which all members have the same objective yet different information. In static team problems, the […]
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Large language models (LLMs) have advanced the frontiers of AI reasoning: they can synthesize information from multiple sources, derive new conclusions, and explain those conclusions to their users. However, LLMs do not do this reliably. They hallucinate facts, convincingly state incorrect deductions, and exhibit logical fallacies like confirmation bias. In this talk, I will describe […] |
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The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the ever-changing state of the world. However, the development of such models demands significant data and computing resources. Moreover, as these models increasingly interact with humans, […]
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Promises are cheap. Software vendors routinely describe their offerings as “secure”, but few are based on designs that can guarantee even the most basic security properties. To address this problem, services like Cloudflare, Android, and Firefox are increasingly relying on languages like Rust and WebAssembly to provide safety by design. But these promises too can […] |
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The rapid adoption of generative AI has created a cycle where personal information cascades perpetually: from people to models to applications and online platforms, then back through scrapers into the system. Simple blanket rules such as "don't train on this data" or "don't share sensitive information" are inadequate, as we face training data scarcity while […]
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Special location for this talk: 105 Amy Gutmann Hall Recent advances in Artificial Intelligence are powering revolutionary interactive tools that will transform the very nature of the scientific enterprise. We describe several large-scale projects at the Allen Institute for AI aimed at developing open models, agentic platforms, and novel interaction paradigms in order to amplify […] |
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