Recently started working at Sanofi to apply cutting-edge computation, AI/ML/DL, and structural biology technologies to resolve challenges in real-world drug discovery.
As a part of my PhD at MIT, I developed state-of-the-art algorithms and methodologies for uncertainty quantification, Bayesian learning, deep learning, and numerical methods for predictive ocean applications. The algorithms developed in my research were problem agnostic, and could be widely applied.
My unique background in mechanical engineering, computational biology, applied mathematics, and computing position me to identify academically understood cross-disciplinary solutions and translate them to solve real-world challenges.
PhD in Mechanical Engineering and Computation, 2022
Massachusetts Institute of Technology
Master's and Bachelor's in Mechanical Engineering, 2016
Indian Institute of Technology Kanpur
Developed a novel, versatile, and rigorous methodology to learn non-Markovian closure parameterizations for low-fidelity models using data from high-fidelity simulations.
Investigation of a method for efficiently updating a reduced order model online, as new data becomes available corresponding to system changes.
Investigating weighted mix-ture of GPs method for handling multiscale features.
Implementation of Machine Learning algorithms for solving nonlinear partial differential equations, in a hybrid framework.