Organizers

Nived Rajaraman is a postdoctoral researcher at Microsoft Research. His research focuses on a variety of topics in the theory and practice of machine learning with a general focus on the statistical and computational aspects of adaptive decision making and reinforcement learning, in the context of understanding of large language models. He has contributed to the academic community as an organizer of the BLISS and CLIMB seminars while a graduate student at Berkeley.

Ayush Sekhari is currently a research scientist with the Chan Zuckerberg Initiative. He was previously a postdoctoral researcher at MIT and received his Ph.D. from Cornell University. His research interests span optimization, online learning, reinforcement learning and control, and the interplay between them. He was awarded the President’s gold medal at IIT Kanpur. His work has been recognized by a best student paper award at COLT (2019). He also serves the community as an organizing committee member of the Learning Theory Alliance.

Bingbin Liu is currently a postdoc research fellow at the Kempner Institute at Harvard University, working closely with the ML Foundations group. Her research aims to make machine learning methods more efficient and accessible. Her work broadly aims to bridge theoretical and empirical/scientific understanding of machine learning, often drawing insights from synthetic “sandbox” settings. Before Kempner, Bingbin was a postdoc fellow at the Simons Institute, participating in Special Year of Language Models and Modern Paradigm of Generalization. She got her PhD from the Machine Learning Department at Carnegie Mellon University, advised by Prof. Andrej Risteski and Prof. Pradeep Ravikumar.

Akshay Krishnamurthy is a senior principal research manager at Microsoft Research, New York City. Previously, he spent two years as an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst and a year as a postdoctoral researcher at Microsoft Research, NYC. His research interests are in machine learning and statistics, with a focus on interactive learning, or learning settings that involve feedback-driven data collection. His recent interests revolve around decision making problems with limited feedback, including contextual bandits and reinforcement learning.

Dylan Foster is a principal researcher at Microsoft Research, New England (and New York City) where he is a member of the Reinforcement Learning Group. Previously, he was a postdoctoral fellow at the MIT Institute for Foundations of Data Science, and received his PhD in computer science from Cornell University, advised by Karthik Sridharan. His research focuses on problems at the intersection of machine learning, AI and interactive decision making. He has received several awards for his work, including the best paper award at COLT (2019) and best student paper award at COLT (2018, 2019).