Trevor Campbell - "Automating Statistical Inference for Modern Probabilistic Models

Time: Wednesday, April 23rd, 12:30-1:30 pm

Speaker: Trevor Campbell (University of British Columbia)

Abstract: How do you reconstruct an image of a black hole using only noisy telescope measurements in the Fourier domain? How do you predict the cure state of a carbon fiber aircraft wing as it cures in an autoclave using a few faulty thermocouples? In theory, Bayesian probabilistic models are the tool for the job: they can capture the complex latent relationships, realistic data generating mechanisms, and challenging uncertainty structure exhibited by these modern inference problems. But in practice, computational methods for Bayesian inference often fail silently on these sorts of problems, even after significant expert tuning. In this talk, I’ll present a few recent advances in Bayesian computational inference from my group—variational nonreversible parallel tempering and autostep involutive Markov chain methods—that provide reliable and efficient inferential results with little to no user input, and are already having an impact in real scientific problems.

Bio: Trevor Campbell is an associate professor in the Department of Statistics at the University of British Columbia. He was previously a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT advised by Tamara Broderick, a Ph.D. candidate in the Laboratory for Information and Decision Systems (LIDS) at MIT advised by Jonathan How, and an undergraduate student at the University of Toronto in aerospace engineering. His research focuses on automated, scalable Bayesian inference algorithms, variational methods, Bayesian nonparametrics, and Bayesian theory. He has received the Blackwell-Rosenbluth award from the International Society for Bayesian Analysis, the PIMS/UBC Mathematical Sciences Early Career Award from the Pacific Institute for the Mathematical Sciences, and an Open Educational Resources Excellence and Impact Award from UBC for his textbook, Data Science: A First Introduction.