Payel Das | Source | Principal Research Staff Member and a manager at IBM Research AI, IBM at IBM

Dr. Payel Das is a Principal Research Staff Member and manager in the Trusted AI Department of IBM Thomas J Watson Research Center in Yorktown Heights, NY. She is also an adjunct associate professor at the department of Applied Physics and Applied Mathematics (APAM), Columbia University. She received her Ph.D. degree in theoretical physics at Rice University, Houston in 2007.

She has been a visiting fellow at the Institute of Pure and Applied Mathematics (IPAM) at UCLA and a visiting student in Princeton University. Her research interest is at the interface of artificial intelligence (AI), statistical physics, and neuroscience.

In her current role, she technically leads and manages research projects related to machine creativity and trusted data modeling and generation. The final goal is to develop statistical machine learning frameworks that ensure trust and enable acceleration in scientific and enterprise applications. Often those machine learning models are augmented with domain knowledge or rules, as well as with brain-based mechanism.

A major focus is to develop novel and controllable deep generative models for heterogeneous data with limited label availability, which is abundant in real world applications. In addition, she is involved in developing interpretable and generalizable ML algorithms. Areas explored are unsupervised & semi-supervised learning, transfer learning, active learning, blackbox optimization, geometric deep learning, and deep generative models. Some recent applications are on physical and biological sciences with the goal of accelerating discovery and responsible deployment.