Payel Das  Payel Das photo         

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Data Sciences and AI Solutions, Technical Lead
Thomas J. Watson Research Center, Yorktown Heights, NY USA
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Professional Associations

Professional Associations:  American Chemical Society  |  American Physical Society (APS)  |  Biophysical Society  |  New York Academy of Science

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Dr. Payel Das is a Research Staff Member and technical lead in the AI Solutions 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 B.Sc. degree from Presidency College, Kolkata and M.Sc. degree from Indian Institute of Technology, Chennai in India. In 2002, Das came to USA to pursue a Ph.D. degree in theoretical physical chemistry with Prof. Cecilia Clementi at Rice University, Houston. Her Ph.D. thesis work was at the intersection of statistical physics and machine learning. During her Ph.D., she developed coarse-grained energy function and novel sampling techniques to directly explore low-dimensional manifold of a very complex, dynamic problem, such as protein folding.

She has been a visiting fellow at the Institute of Pure and Applied Mathematics (IPAM) at UCLA, where she worked on extracting efficetive dimensionality of high-dimensional systems. She has also been a visting student in Princeton University, where she worked with Prof. Yannis Kevrekidis on developing a multi-scale simulation methodology, based on data mining tools for inferring low-dimensional reduction coordinates. During her years in India, she was a summer intern at National Chemical Laboratory, Pune, with Prof. Sourav Pal, where she worked on theoretical investigation of hard-soft acid base relation. Her research interest is in understanding deep neural networks from a statistical mechanics perspective as well as in developing novel interpretable deep learning algorthms.


In her current role, she technically leads and manages research projects related to computational creativity, with applications in material science, chemistry, biology, and neuroscience. These projects lie at the intersection of data-driven and physics-based  modeling. A major focus is to develop novel deep generative models for heterogenous data, which is abundant in real world applications. Prior to her current role, Dr. Das was a post-doctoral researcher at the Computational Biology Center at IBM TJ Watson Research, where she worked on free energy perturbation theory to study disease mechanisms.

 

Das has co-authored over 30 peer-reviewed publications and several patent disclosures, given dozens of invited talks at several university colloquiums, department seminars, top rated conferences, and workshops. She serves in the editorial advisory board member of the ACS Central Science journal. Das is the recipient of IBM Outstanding Technical Achievement Award (the highest technical award at IBM), two IBM Research Division Awards, one IBM Eminence and Excellence Award, and one IBM Invention Achievement Award.