Roman Holenstein

Roman obtained his Ph.D. in Computer Science from University of British Columbia. He worked on Monte Carlo framework method based on Markov chain and sequential Monte Carlo for efficient sampling from high-dimensional distributions. Roman specializes in Monte Carlo methods (MCMC, SMC), Bayesian statistics, machine learning, natural language understanding. He previously worked as a Senior Sceintist with Bing query understanding team at Microsoft.