The MIDAS affiliate faculty community consists of 340 U-M faculty members from over 60 departments.

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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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

Jing Sun

adaptation, classification, estimation, modeling, predictive control, real-time optimization

Veera Sundararaghavan

Integrated Computational Materials Engineering (ICME), Materials Informatics and Quantum Computing.

Rie Suzuki

Social factors of health behaviors and health outcomes

Kean Ming Tan

Statistical Machine Learning, biomedical data

Jeremy M G Taylor

Statistical methodology for cancer research

Stephanie Teasley

Sociotechnical systems for collaboration and successful learning outcomes

Misha Teplitskiy

computational methods, experimental methods, science of science

Jonathan Terhorst

computational population genetics

Ambuj Tewari

Statistical methods for sequential decision making in personalized health