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.


Filter

Methodologies

Applications

Filter by last name:

  • All
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z

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

Quentin Stout

Scalable parallel algorithms for large scientific problems

Martin J. Strauss

Randomized approximation algorithms for massive data sets

Wencong Su

Efficient management of a large number of devices through distributed intelligence

Vijay Subramanian

Stochastic modeling, decision and control theory and applications to networks

Yuekai Sun

Statistical methodology for high-dimensional problems

Rie Suzuki

Social factors of health behaviors and health outcomes

Jeremy M G Taylor

Statistical methodology for cancer research