I’m a masters (Robotics) student at Carnegie Mellon University (CMU) advised by Prof. Ben Eysenbach, Prof. Ruslan Salakhutdinov, and Prof. Jeff Schneider. I am broadly interested in machine learning problems and their applications in computer vision and robotics.
I am currently a student researcher in the DARPA RACER project, where I am working on reinforcement learning methods for autonomous navigation in off-road environments. I spent the summer of 2022 as an ML intern in the Maneuver Planning team at Cruise. Before joining CMU, I was a research intern in TensorLab (Caltech), where I closely collaborated with Prof. Kamyar Azizzadenesheli (Purdue University), Prof. Anima Anandkumar (Caltech), Prof. Yisong Yue (Caltech), and Dr. Mohammad Ghavamzadeh (Google Research). I received my Bachelor's degree from IIT Roorkee, with a major in Electronics and Communication Engineering and minors in Computer Science.
An introduction to Bayesian quadrature for policy gradient estimation, and the computation methods required for scaling it to high-dimensional settings. Introduces two policy gradient estimators, (i) Deep Bayesian Quadrature Policy Gradient (DBQPG) and (ii) Uncertainty Aware Policy Gradient (UAPG), that serve as a drop-in replacement for Monte-Carlo estimation in most policy gradient algorithms while offering more accurate policy gradient estimates, superior sample complexity, and higher average return.
[Jul 2023] "Distributional Distance Classifiers for Goal-Conditioned Reinforcement Learning" accepted to the New Frontiers in Learning, Control, and Dynamical Systems Workshop, ICML 2023.
[May 2022] Starting as an ML Engineer Intern in the Maneuver Planning team at Cruise.
[Aug 2021] Starting my M.S. Robotics (MSR) degree at the Robotics Institute, Carnegie Mellon University (CMU).
[Aug 2021] Attended Machine Learning Summer School (MLSS) Taipei.
[Dec 2020] "Deep Bayesian Quadrature Policy Optimization" accepted to AAAI 2021.
[Oct 2020] "Deep Bayesian Quadrature Policy Optimization" accepted to NeurIPS Deep RL and Real-World RL workshops.
[Sep 2020] "Reinforced Multi-task Approach for Multi-hop Question Generation" accepted to COLING 2020.
[May 2020] " Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution" accepted to IJCNN 2020.
[Oct 2019] Serving as Secondary Reviewer for AAAI 2020.
[Sept 2019] Serving as Secondary Reviewer for Machine Learning and the Physical Sciences (ML4PS), NeurIPS (2019) workshop.