Synthetic data for reliable AI
Synthetic data can unlocks unlimited targeted data for training and evaluating AI systems. From distillation to synthetic environments, synthetic data can be quite powerful when real data is scare. Yet, synthetic data inherently introduces a gap between real world data that can lead to unpredictable behavior during deployment. Is synthetic data the way?
Come hear from leading researchers and practitioners in-person in New York.
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Featured Speakers
Gautam Kamath
Gautam Kamath is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He is also a Faculty Member at the Vector Institute, and on leave from the University of Waterloo. He has a B.S. in Computer Science and Electrical and Computer Engineering from Cornell University, and an M.S. and Ph.D. in Computer Science from the Massachusetts Institute of Technology. He is interested in reliable and trustworthy statistics and machine learning, including considerations such as data privacy and robustness. He was a Microsoft Research Fellow, as a part of the Simons-Berkeley Research Fellowship Program at the Simons Institute for the Theory of Computing. He serves as an Editor in Chief of Transactions on Machine Learning Research, and was the program committee co-chair of the 36th International Conference on Algorithmic Learning Theory (ALT 2025). He is on the board of several machine learning conferences, including ICML, COLT, ALT, FORC, and is the steering committee chair of SaTML. He is the recipient of several awards, including the Presburger Award, the Godel Prize, the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies, a best paper award at the Forty-first International Conference on Machine Learning (ICML 2024), an Ontario Early Researcher Award, and the Faculty of Math Golden Jubilee Research Excellence Award.
Shubham Toshniwal
Shubham is a Senior Research Scientist at Databricks Mosaic in New York City. His research focuses on using synthetic data and reinforcement learning to train custom knowledge agents. He has been a primary contributor to projects such as KARL, a state-of-the-art enterprise knowledge agent, and Instructed-Retriever-1, which accelerated search in Databricks Knowledge Assistant by more than 3×.
Previously, he worked at NVIDIA on the Nemotron model family and the OpenMath series, and at FAIR, Meta AI. He received his Ph.D. from TTI Chicago, advised by Kevin Gimpel and Karen Livescu, and his B.Tech. in Computer Science from IIT Kanpur.