Professional AssociationsProfessional 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 Data Science Department of IBM TJ Watson Research Center. She is a quantitative scientist with 13+ years of experience in modeling, simulation, and machine learning, with a passion for identifying interesting patterns from huge, complex datasets. Dr. Das has a unique technical background spanning theoretical chemistry, high-performance computing, multi-scale modeling and simulations, and machine learning.
In her current role, she technically leads and manages research projects, with the aim of developing therapies or methods that will save and improve human lives. Prior to her current role, Dr. Das was a post-doctoral researcher at the Computational Biology Center at IBM TJ Watson Research Center, where she worked on uncovering the molecular mechanism of infectious and degenerative diseases by using large-scale computer simulations. Das received her PhD degree from the Rice University, USA, where she worked on the development of multi-scale modeling and machine learning algorithms for solving the protein folding problem. During her internship at National Chemical Laboratory, Pune, she worked on ab initio investigation of chemical reactivity using hard-soft acid base relation.
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. Her research interest focuses on bridging the gap between physics-based and data-driven modeling of complex systems, with applications in chemistry, biology, and neuroscience.