Jalaj is a PhD student in Operations Research at Columbia University, working with Prof. Daniel Russo. His thesis work explores foundations of modern Reinforcement Learning (RL) algorithms using ideas from optimization theory. In the past, he has also done research work on Bayesian Machine learning methods, specifically in designing computationally efficient Markov Chain Monte Carlo (MCMC) algorithms for posterior sampling. He is excited about applying RL and machine learning methods to problems of practical interest, for example in the areas of healthcare, neuroscience, autonomous systems, personalized Ads and more.
Prior to Columbia, Jalaj graduated from India Institute of Technology (IIT), Delhi in 2012 with a B.Tech in Industrial Engineering and Operations Research.