Apoorv Srivastava

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.