Towards Automated Machine Learning

Bayesian modeling is changing the way we do science. We want to study large datasets to shed light onto natural processes. (How do proteins function? How do social networks form?) To this end, we need tools to rapidly and iteratively explore hidden patterns in data. However, using Bayesian models to infer such patterns requires enormous computational effort and cross-disciplinary expertise. My goal is to develop easy-to-use machine learning tools that empower scientists to gain new insights from data. In this talk, I will describe some of my recent research in new mathematical approaches to automating inference and building effective Bayesian models.

Alp is a postdoctoral research scientist at the Data Science Institute and the department of Computer Science at Columbia University. He works with David Blei on developing scalable and robust machine learning tools. He collaborates with Andrew Gelman on the Stan probabilistic programming system. Alp received his Ph.D. from Yale University, where he was awarded the Becton prize for best thesis in engineering and applied science. He holds a B.A.Sc. from the University of Toronto.

LIGHT LUNCH WILL BE SERVED