I am a Staff Applied Researcher in LinkedIn's Relevance Sciences and AI team working on Statistical Learning and Large Scale Optimization problems across different products. Recently, I have been working on Bayesian Optimization, Automatic Hyperparameter Tuning, Causal Inference on Networks and Optimal Marketplace.

Before joining LinkedIn, I finished my PhD in the Department of Statistics at Stanford University advised by Prof. Art Owen. My dissertation was on Quasi-Monte Carlo Methods in Non-Cubical Spaces. Before joining the doctoral programme, I did my B.Stat (Hons) and M.Stat (Specializing in Mathematical Statistics and Probability) from Indian Statistical Institute, Kolkata. In my final year of M.Stat., I was supervised by Prof. Debapriya Sengupta towards my thesis on density estimation with discontinuities.

Before coming to Stanford I did an internship at McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins University School of Medicine under the guidance of Prof. Aravinda Chakrabarti. I also did an internship at Deutsche Bank as a financial analyst in the Global Capital Markets group, working towards a dependency structure in several stock markets.

During my time at Stanford, I was extremely fortunate to have had several collaborations with the LinkedIn Relevance Sciences Team, including two internships in the Content Relevance Team and the Multi-Objective Optimization team during the Summer of 2014 and 2015 respectively.

I am generally interested are in Statistical Theory, Discrepancy Theory, Monte Carlo Methods, Numerical techniques, Bayesian Optimization and Large-Scale Optimization.