Better Science Through Better Bayesian Computation

As we grapple with the hype of "big data" in computer science, it is important to remember that the data are not the central objects: we collect data to answer questions and inform decisions in science, engineering, policy, and beyond.  In this talk, I will discuss my work in developing tools for large-scale data analysis, and the scientific collaborations in neuroscience, chemistry, and astronomy that motivate me and keep this work grounded.  I will focus on two lines of research that I believe capture an important dichotomy in my work and in modern probabilistic modeling more generally: identifying the "best" hypothesis versus incorporating hypothesis uncertainty.  In the first case, I will discuss my recent work in Bayesian optimization, which has become the state-of-the-art technique for automatically tuning machine learning algorithms, finding use across academia and industry. In the second case, I will discuss scalable Markov chain Monte Carlo and the new technique of Firefly Monte Carlo, which is the first provably correct MCMC algorithm that can take advantage of subsets of data.

Ryan Adams is an Assistant Professor of Computer Science at Harvard University, in the School of Engineering and Applied Sciences. He leads the Harvard Intelligent Probabilistic Systems group, whose research focuses on machine learning and computational statistics, with applied collaborations across the sciences.  Ryan received his undergraduate training in EECS at MIT and completed his Ph.D. in Physics at Cambridge University as a Gates Cambridge Scholar under David MacKay.  He was a CIFAR Junior Research Fellow at the University of Toronto before joining the faculty at Harvard.  His Ph.D. thesis received Honorable Mention for the Leonard J. Savage Award for Bayesian Theory and Methods from the International Society for Bayesian Analysis.  Ryan has won paper awards at ICML, AISTATS, and UAI, and received the DARPA Young Faculty Award.