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Tuseeta Banerjee

Tuseeta Banerjee

Research Scientist, Machine Learning

Tuseeta Banerjee is a Research Scientist in the 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 Mathematica programming with a focus on statistics and deep learning.

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


Neural Networks: An Introduction  May 2, 2019

Deep Learning and Computer Vision: Converting Models for the Wolfram Neural Net Repository  December 6, 2018