Fox School of Business · Temple University
Assistant Professor of Statistical Science
Academic Director, Master of Science in Business Analytics
Department of Statistics, Operations, and Data Science
Ken is an Assistant Professor of Statistical Science in the Department of Statistics, Operations, and Data Science at Temple University, Fox School of Business. He is also Academic Director of the Master of Science in Business Analytics program (since July 2026). Before Temple, he was a Senior Research Professional in Econometrics and Statistics at The University of Chicago, Booth School of Business. He received his Ph.D. in statistical science at Duke University in the Department of Statistical Science and his Ph.D. in Economics at Keio University in the Department of Economics. He also has a M.S. in statistical science from Duke University and a dual masters from Keio University (Economics) and L'Institut d'Études Politiques de Paris (Sciences Po) (Economics and Public Policy, joint with Ecole Polytechnique and ENSAE). As an undergraduate at Keio University, he received a B.A. in Economics with a focus on Bayesian econometrics and film theory.
Ken’s research develops Bayesian methods for ensembling, dynamic causal inference, and statistical decision making under uncertainty. His work on Bayesian predictive synthesis learns from the biases and dependencies of multiple predictive distributions. He studies how interventions change outcomes over time, how statistical procedures should be designed and evaluated, and how probability, models, and Bayesian agents should be interpreted and used.
Several models and forecasters can provide useful but incomplete information about the same outcome. Bayesian predictive synthesis combines their predictive distributions while learning about biases, calibration, and dependence, allowing the contribution of each source to change over time. Related synthesis methods support causal inference by combining treatment-effect estimates, propensity-score models, and outcome models.
An intervention can change both outcomes and the way they develop over time. Dynamic causal inference studies these changes through potential outcome trajectories, with explicit assumptions for separating intervention effects from other sources of variation in a time series.
Statistical methods involve choices about what to learn, how to evaluate it, and which actions to take. Decision theory makes these choices explicit, connecting cross-validation, prediction, and generalized Bayes to the consequences and uncertainty that matter for a stated purpose.
Statistical conclusions depend on how probabilities, models, and agents are interpreted. Philosophy of statistics studies what these representations mean, how several imperfect models can be used together, and what warrants moving from a coherent calculation to a prediction or decision.