NIH: Office of Disease Prevention

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Medicine: Mind the Gap

Automating Machine Learning for Prevention Research

Jason Moore, Ph.D.

Tuesday, August 29, 2017
11:00 a.m.–12:00 noon ET

Jason Moore, Ph.D. External Website Policy
Director, Institute for Biomedical Informatics
Perelman School of Medicine
University of Pennsylvania

Registration External Website Policy is required for this webinar. You will receive a confirmation email that contains detailed information about joining the webinar.

About the Webinar

Successful disease prevention will depend on modeling human health as a complex system that is dynamic in time and space and driven by biomolecular and physiologic interactions. Machine learning holds promise for embracing this complexity in Big Data. The intent is to provide a complement to traditional statistical methods that ignore much of this complexity in favor of simpler mathematical models. Unfortunately, the barrier to machine learning is steep, requiring knowledge about many different types of algorithms and methods that need to be combined in an analytical pipeline. We review here the new discipline of automated machine learning (AutoML) that has the goal of simplifying this process and making machine learning more accessible. An example from human genetics will be presented.

About Jason Moore, Ph.D.

Jason Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Chief of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth College, where he was Director of the Institute for Quantitative Biomedical Sciences. Prior to Dartmouth, he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University, where he launched their first high-performance computer. He has a Ph.D. in Human Genetics and an M.S. in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American College of Medical Informatics (ACMI), an elected fellow of the American Statistical Association (ASA), and was selected as a Kavli Fellow of the National Academy of Sciences. He is currently a Penn Fellow and serves as Editor-in-Chief of the journal BioData Mining.