Adam Kelleher, Ph.D.
Principal Data Scientist, BuzzFeed
Adam received his Ph.D. in physics and is the Principal Data Scientist at Buzzfeed where he focuses on recommender systems, virality, and information diffusion. He has a strong interest in causal inference and maintains a popular open-source project on GitHub. Adam also teaches causal inference to data scientists at Columbia's Data Science Institute.
While machine learning and predictive models have become part of the mainstream, rigorous causal inference is relatively new to data science. In the past few years, we've grown beyond AB testing, and are starting to adopt more of the rigors of causal inference in the social sciences. This presents significant problems and opportunities for science. Adam will review some of the challenges transitioning into causal inference many of which are institutional. He will also outline some of the more interesting opportunities, as well, and talk about his experience doing causal inference at one of the fastest growing startups globally.
James Faghmous, Ph.D. is an Assistant Professor in Health System Design and Global Health at the Icahn School of Medicine at Mount Sinai. He is also the founding CTO of the Arnhold Institute for Global Health. He works on achieving health equity using data science. Connect with him (at your own risk) using those tiny social icons.