Around 260 U-M faculty members from over 60 departments are affiliated with the Michigan Institute for Data Science, providing them access to collaboration opportunities, support for research funding submissions, and updates on data science news and events at U-M and beyond.

Use this box to search by name, department, or other keyword. Use the filters below to search by major data science methodologies or applications.


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Clayton Scott

Quantitative predictions and inferences about large, complex data

Lawrence Seiford

Quality engineering, productivity analysis and process improvement

Ananda Sen

Competing risks, and inter-rater agreement, recurrent event data

Srijan Sen

Gene-environment interaction and their effect on stress, anxiety and depression

Matthew Shapiro

Improving the quality of national economic statistics

Taysseer Sharaf

Developing statistical methods using artificial intelligence

Kerby Shedden

Applied statistics, data science and computing with data

Siqian Shen

Optimization and risk analysis of energy, cloud-computing and transportation, healthcare

Jie Shen

Digital diagnosis of material damage based on large-scale data

Yuki Shiraito

Bayesian statistical models and large-scale computational algorithms for political science

Ginger Shultz

Analysis of course placement, skills transfer and scientific reasoning

Mehrdad Simkani

Rational approximation in the complex domain

Stephen Smith

Using large data to examine rates and modes of evolution

Peter X. K. Song

Theory and methodology for environmental health sciences and nutritional sciences

S. Sriram

Brand and product portfolio management

Stilian A. Stoev

Applied probability and statistics for stochastic processes