American Statistical Association
415 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: bayesian multi-level modeling, spatial statistics, urban analytics, statistics in sports
Links: CV
PhD, Harvard University, 2004
AM, Harvard University, 2001
MS, McGill University, 1999
BS, McGill University, 1997
Savage Award for best thesis, International Society for Bayesian Analysis (2005)
David W. Hauck Award for Outstanding Teaching (2009)
Sports in Statistics Award, American Statistical Association (2011)
JASA Reproducibility Award, American Statistical Association (2023)
Wharton: 2004-present
Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.
Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.
Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package. Knowledge of high school algebra is required for this course.
Written permission of instructor and the department course coordinator required to enroll in this course.
The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.
Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.
Written permission of instructor, the department MBA advisor and course coordinator required to enroll.
This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.
Dissertation
Written permission of instructor and the department course coordinator required to enroll.
American Statistical Association
for the paper “OpenWAR: an open source system for evaluating overall player performance in major league baseball.”
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