Our Speakers and Panelists

Michael Albergo is a Junior Fellow at the Harvard Society of Fellows. Starting in 2026, he will be an assistant professor of applied mathematics and Kempner Institute investigator at the university. His research focuses on the design of machine learning algorithms for the study of the natural world and takes inspiration from phenomena in probability, statistical physics, and biology. He completed his PhD at New York University in 2024.

Sitan Chen is an Assistant Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences, where he is a member of the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative. He works on designing algorithms with provable guarantees for fundamental problems in data science, especially in the context of generative modeling, deep learning, and quantum information. Previously he was an NSF postdoc at UC Berkeley under the wise guidance of Prasad Raghavendra. He received my PhD in EECS from MIT as a member of CSAIL and the Theory of Computation group, advised by Ankur Moitra and supported by an MIT Presidential Fellowship and a PD Soros Fellowship. Prior to MIT,  he studied mathematics and computer science as an undergraduate at Harvard, where he worked with Salil Vadhan and Leslie Valiant.

Ricky Tian Qi Chen is a research scientist at Meta Fundamental AI Research (FAIR) team in New York. His research is on building simplified abstractions of the world through the lens of dynamical systems and flows. Lately, He has been exploring the use of stochastic control theory for large-scale generative modeling , as well as constructing discrete generative models through continuous-time Markov chains. His methods such as have been applied successfully for foundation models of video and audio. Ricky received his PhD in Computer Science from the University of Toronto, advised by David Duvenaud.

Michael Hutchinson  is a Research Scientist at Isomorphic Labs where he works on reimagining drug discovery with artificial intelligence. He received his PhD from the University of Oxford, supervised by Yee Whye Teh and Max Welling, where his work focused on developing new methods for scientific machine learning, specifically on geometric deep learning and generative modelling, with a focus on diffusion models on manifolds. 

Marylou Gabrié is an assistant professor at the Physics Department of École Normale Supérieure since 2024. Prior to that, she was an Assistant Professor in Applied Math at École Polytechnique and she held a postdoctoral appointments with the Center for Data Science at New York University and the Flatiron’s Center for Computational Mathematics (CCM). Her research lies at the boundary of machine learning and statistical physics. She is currently interested in leveraging machine learning methods in statistical physics computing problems.

Emtiyaz Khan is a team leader (tenured) at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference (ABI) Team. Since July 2024, he has been a visiting professor at Technical University, Darmstadt (Germany). He works on problems in several areas of machine learning, such as approximate inference, deep learning, reinforcement learning, active learning, online learning, and reasoning in computer vision. In my recent works,  he has worked on ideas from a wide range of fields, such as optimization, Bayesian statistics, information geometry, signal processing, and control systems.

Grant M. Rotskoff is an Assistant Professor of Chemistry at Stanford. He studies the nonequilibrium dynamics of living matter with a particular focus on self-organization from the molecular to the cellular scale. His work involves developing theoretical and computational tools that can probe and predict the properties of physical systems driven away from equilibrium. Recently, he has focused on characterizing and designing physically accurate machine learning techniques for biophysical modeling. Prior to his current position, Grant was a James S. McDonnell Fellow working at the Courant Institute of Mathematical Sciences at New York University. He completed his Ph.D. at the University of California, Berkeley in the Biophysics graduate group supported by an NSF Graduate Research Fellowship.

Molei Tao is an associate professor in the School of Mathematics at Georgia Tech. He received his PhD in Control & Dynamical Systems with a minor in Physics in 2011 from Caltech, advised by Houman Owhadi and Jerry Marsden. Afterwards, he worked as a postdoc in Computing & Mathematical Sciences at Caltech from 2011 to 2012, and then as a Courant Instructor at NYU from 2012 to 2014. From 2014 on, he has been an assistant, and then associate professor at Georgia Tech.  He is interested in the theoretical and algorithmic foundation of machine learning including sampling, optimal transport, and (diffusion) generative modelling, as well as AI4Science applications

Pranav Murugan Pranav is a senior ML research scientist at Genesis Research, the research arm of Genesis Therapeutics. His current work is focused on the creation of foundation generative models for drug discovery, ranging from architecture design, training and finetuning, and systems-level optimizations to unlock further scale and performance. Pranav received his S.B. and M. Eng. from MIT, where he studied computer science and physics with a research focus on applying statistical physics and stochastic methods to understand virus evolution and design better vaccines.

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University. He received his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley, in 2018, and his PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023, advised by Philippe Rigollet. In Fall 2021, he participated in the Simons Institute program on Geometric Methods in Optimization and Sampling and co-organized (with Kevin Tian) a working group on the complexity of sampling. In Spring 2022, he visited Jonathan Niles-Weed at New York University. In Summer 2022, he was a research intern at Microsoft Research, supervised by Sébastien Bubeck and Adil Salim. In Fall 2023 and Spring 2024, he was a postdoctoral researcher at the Institute for Advanced Study.

Francisco Vargas is a PhD student in the computer Laboratory at Cambridge and an ML Research Scientist at Xaira Therapeutics. His research focuses on topics at the interface of stochastic control and inference. His previous appointments  include Research Scientist Intern at Google Deepmind, AI Research Intern at Google-X, and Applied Scientist Intern at Amazon Alexa Research.