Hello!
I am a final year Ph.D. candidate in Mechanical Engineering at Stanford University, advised by Prof. Daniel Tartakovsky and Prof. Eric Darve. My research focuses on inverse problems, data assimilation (DA), uncertainty quantification (UQ), and probabilistic estimation in highly nonlinear dynamical systems. I develop optimization and machine learning methods for state estimation, forecasting, and sequential decision-making under uncertainty, with current work centered on deep probabilistic models for particle filtering and the assimilation of sparse and binary observations.
My work combines tools from optimization, machine learning, scientific computing, and stochastic modeling, with applications spanning computational engineering, forecasting, and control. I am broadly interested in scalable and data-efficient inference algorithms for high-dimensional systems.
Alongside my academic research, I have worked on optimization, forecasting, and quantitative modeling problems in both research and industry settings, including internships at Qube Research & Technologies, Los Alamos National Laboratory, and EPFL.
In June 2023, I received a Master of Science (MS) degree in Mechanical Engineering with a specialization in automatic and learning-based control from Stanford University. Prior to Stanford, I completed my Bachelor of Technology (B.Tech.) with Honors at IIT Bombay.
More details about my research can be found on the Research Page.
