My research interests in machine learning are motivated by addressing challenges for autonomous solutions to real-world problems. Specifically, I work on the theoretical foundations of reinforcement learning algorithms, tackling the simulation to real-world performance gaps by providing robust solutions. My future research will address such environmental uncertainties in more general domains, like multi-agent, imitation learning, human feedback, and more systems. In addition to statistical reinforcement learning, my daily drives and expertise span several areas like optimization, high-dimensional probability, multi-armed bandits, online learning, and stochastic theory.

I am a Postdoctoral Scholar Research Associate in Computing and Mathematical Sciences department in the Division of Engineering and Applied Science at California Institute of Technology (Caltech), hosted by Prof. Adam Wierman and Prof. Eric Mazumdar. I have been honored as a recipient of the PIMCO Postdoctoral Fellow in Data Science award. Before joining for the postdoc, I got my Ph.D. at Texas A&M University in August 2023, where I had the privilege of being advised by Prof. Dileep Kalathil.  

Prior to joining the PhD program, I worked for Qualcomm Bengaluru in the Corporate Research and Development division. Before that, I completed my Masters in Communication and Networks from the Department of Electrical Communication Engineering at the Indian Institute of Science (IISc) under the guidance of Prof. Rajesh Sundaresan. It was during my master's program at IISc that I got introduced to the world of research. I am truly grateful to Rajesh for paving the way in this rich world called academic research. I completed my Bachelors from the Department of Electronics and Communication Engineering at PES Institute of Technology (now PES University) in India.

I am in the job market for full-time faculty positions and core industrial research positions starting in 2025. Please feel free to reach out! More details are under the Job Market tab.