Marc Suchard, MD, PhD
Professor, Department of Biomathematics, David Geffen School of Medicine
University of California, Los Angeles
Dr. Suchard is in the forefront of high-performance statistical computing. He is a leading Bayesian statistician who focuses on inference of stochastic processes in biomedical research and in the clinical application of statistics. His training in both Medicine and Applied Probability help bridge the gap of understanding between statistical theory and clinical practicality. He has been awarded several prestigious statistical awards such as the Savage Award (2003), the Mitchell Prize (2006 and 2011), as well as an Alfred P. Sloan Research Fellowship (2007) in computational and molecular evolutionary biology and a Guggenheim Fellowship (2008) to further computational statistics. He recently received the Raymond J. Carroll Young Investigator Award (2011) for a leading statistician within 10 years post-Ph.D., and in 2013 he was honored with the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award for outstanding contributions to the statistics profession by a person aged 40 or under. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics.
Schuemie MJ, Ryan PB, Suchard MA, Shahn Z, Madigan D. Discussion: An estimate of the science-wise false discovery rate and application to the top medical literature. Biostatistics. 2014 Jan;15(1):36-9; discussion 9-45. doi: 10.1093/biostatistics/kxt037. Epub 2013 Sep 25.
Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2014 Jan 30;33(2):209-18. doi: 10.1002/sim.5925. Epub 2013 Jul 30.
Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics. 2014 Apr;15(2):207-21. doi: 10.1093/biostatistics/kxt043. Epub 2013 Oct 4.
Madigan D, Stang PE, Berlin JA, et al. A Systematic Statistical Approach to Evaluating Evidence from Observational Studies. Annual Review of Statistics and Its Application. 2014;1(1):11-39.
Reich CG, Ryan PB, Suchard MA. The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S195-204. doi: 10.1007/s40264-013-0112-0.
Ryan PB, Stang PE, Overhage JM, et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 2013 Oct;36(Suppl 1):S143-58. doi: 10.1007/s40264-013-0108-9.
Suchard MA, Zorych I, Simpson SE, Schuemie MJ, Ryan PB, Madigan D. Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S83-93. doi: 10.1007/s40264-013-0100-4.
Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics. 2013 Dec;69(4):893-902. doi: 10.1111/biom.12078. Epub 2013 Oct 11.
Madigan D, Ryan PB, Schuemie M, et al. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol. 2013 Aug 15;178(4):645-51. doi: 10.1093/aje/kwt010. Epub 2013 May 5.
Ryan P, Suchard MA, Schuemie M, Madigan D. Learning From Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products. Statistics in Biopharmaceutical Research. 2013;5(3).
Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D. Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model. ACM Trans Model Comput Simul. 2013;23(1):1-17.
Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics, 15, 207-221, 2014. PMID: 24096388
Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D. Massive parallelization of serial inference algorithms for complex generalized linear models. ACM Transactions on Modeling and Computer Simulation, 23, 10, 2013. NIHMSID: 607868
Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology, 61, 539-542, 2012. PMCID: PMC3329765
Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Molecular Biology and Evolution, 29, 1969-1973, 2012. PMCID: PMC3408070
Suchard MA and Rambaut A. Many-core algorithms for statistical phylogenetics. Bioinformatics, 25, 1370-1376, 2009. PMCID: PMC2682525
