Marc A. Suchard



Professor Marc A. Suchard

Marc A. Suchard is a Professor in the Departments of Biostatistics, of Biomathematics and of Human Genetics in the UCLA Fielding School of Public Health and David Geffen School of Medicine at UCLA. He earned his Ph.D. in biomathematics from UCLA in 2002 and continued for a M.D. degree which he received in 2004.  Dr. Suchard is a leading Bayesian statistician who focuses on inference of stochastic processes in genomics and for massive datasets in healthcare.  His training in both medicine and applied probability help to bridge the gap of understanding between statistical theory and clinical practicality.  Dr. Suchard has been awarded several prestigious statistical awards such as the 2003 Savage Award, the 2006 and 2011 Mitchell Prizes, as well as a 2007 Alfred P. Sloan Research Fellowship in computational and molecular evolutionary biology, and a 2008 Guggenheim Fellowship to further computational statistics.  Finally, he received the 2011 Raymond J. Carroll Young Investigator Award and the 2013 Committee of Presidents of Statistical Societies (COPSS) Presidents' Award for outstanding contributions to the statistics profession by a person aged 40 or under.

 Address:Departments of Biomathematics, Biostatistics and Human Genetics David Geffen School of Medicine University of California at Los Angeles Los Angeles, CA 90095, USA

Email: msuchard@gmail.com, msuchard@ucla.edu


 Presentation:


Title: Inference for Discrete Outcome Stochastic Processes at Scale

Abstract: Researchers struggle with likelihood-based inference from count data that arise continuously in time but we only intermittently observe them.  A major shortcoming lies in our inability to integrate most underlying stochastic processes generating the data over all possible realizations between observations.  Since these processes are ubiquitous across the natural, physical and social sciences as generative models, solutions should promote the use of statistical inference in many real-world problems.  One seemingly trivial example is a stochastic compartmental model tracking the count of susceptible, infectious and removed people during the spread of an infectious disease.  For over 90 years, many have believed the transition probabilities of this SIR model remain beyond reach.  However, applying a novel re-parameterization, integral transforms and other tools from numerical analysis shows that we can compute the transition probabilities in merely quadratic complexity in terms of the observed change in population size.  Other stochastic processes for modest numbers of outcomes, such as those employed to model molecular sequence evolution, yield well to advancing computing technology, such as many-core parallelization.  Examples in this talk stem from the dynamics of influenza across the global and the 2014-2015 West African ebola outbreak.