(734) 936-2619

Business Analytics, Finance Research, Management Science, Marketing and Consumer Behavior Research
Bayesian Methods, Computational Tools for Data Science, Data Mining, Dynamical Models, Econometrics, Machine Learning, Missing Data and Imputation, Predictive Modeling, Statistical Inference, Statistical Modeling, Statistics, Time Series Analysis

Peter Lenk


Technology and Operations, Ross School of Business

Prof. Lenk develops Bayesian models that disaggregate data to address individuals.  He also studies Bayesian nonparametric methods and currently consider shape constraints.  Prof. Lenk teaches and uses data mining methods such as recursive partition and neural networks.