Wolfram Computation Meets Knowledge

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Tuseeta Banerjee

Tuseeta Banerjee

Tuseeta Banerjee is a research scientist with Wolfram’s Machine Learning Team, focusing on applications of neural networks. She imports and implements neural net models in the Wolfram Language for various high-level functions in Mathematica and for the Wolfram Neural Net Repository. In her previous role as a technology engineer, she provided machine learning–based solutions to clients. She is also a certified Wolfram Language instructor, teaching and creating various courses on programming with a focus on statistics and deep learning.

Prior to joining Wolfram, she completed her PhD in 2015 at the University of Illinois at Urbana–Champaign with research in the field of chemical physics and a Computational Science and Engineering Certificate. For her PhD research, she used Monte Carlo–based quantum-classical path integral methods to study models that mimic chemical reactions and photosynthetic reaction centers.

Organization:

Wolfram Research, Inc.

Degrees:

BS in Chemistry, St. Xavier's College, Kolkata, India
MS in Physical Chemistry, Indian Institute of Technology Bombay, Mumbai, India
Certificate of Computational Science and Engineering, University of Illinois at Urbana-Champaign
PhD in Theoretical Chemistry, University of Illinois at Urbana-Champaign

Languages:

English, Bengali, Hindi

Interests:

Cooking, art, music