Bill's current research focuses on statistical computing and Bayesian hierarchical models, including applications to meta-analysis and data mining. He is the inventor of the empirical Bayesian data mining algorithm known as Gamma-Poisson Shrinker (GPS) and its successor MGPS, which have been applied to the detection of safety signals in databases of spontaneous adverse drug event reports. These methods are now used within the FDA and industry. From 1996 through 2004 he was a senior member of the data mining research group at AT&T Labs. Before that, he was Chief Statistical Scientist at BBN Software Products, where he was lead statistical designer of software advisory systems for experimental design and data analysis called RS/Discover and RS/Explore. He has been on the faculties of the University of California at Berkeley, the University of Michigan, MIT, and most recently was Professor of Biostatistics and Medical Informatics at Columbia University from 1994-1996. He has authored approximately fifty papers in peer-reviewed journals and has also been an associate editor of the Journal of the American Statistical Association, Statistics in Medicine, Statistics and Computing, and the Journal of Computational and Graphical Statistics.
OMOP Investigator Role & Research Interests
For the past year, Bill has worked as a principal investigator on the OMOP research program, focusing on the analysis of the OMOP results. He has been working on the OMOP statistical aspects of the design of experiments and data analysis.
Harpaz R, Vilar S, DuMouchel W, Salmasian H, Haerian K, et al. (2012) Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. Journal of the American Medical Informatics Association. DOI: 10.1136/amiajnl-2012-000930.
Harpaz R, Dumouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis. Clin Pharmacol Ther 2012, DOI 10.1038/clpt.2012.50.