Abstract: Flow and diffusion models have become cornerstones of both scientific and industrial generative AI research. These methods work by construction of a dynamics that maps samples from a reference distribution to samples from a target distribution known empirically through data. An open question is how to best control and modify these dynamics so as to satisfy specified target sampling constraints, often specified by a reward or tilting function. I will provide an overview of the mathematics underlying this construction…

This is a joint talk with CSAIL’s Theory of Computation Colloquium. Abstract: Over the last few years, graph-based approaches to nearest neighbor search have attracted renewed interest. Algorithms such as HNSW, NSG, and DiskANN have become popular tools in practice. These algorithms are highly versatile and come with efficient implementations. At the same time, their correctness, performance guarantees, and functionality remain poorly understood. In this talk, I will discuss the challenges and opportunities presented by this class of algorithms. Bio: Dr. Piotr…

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