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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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

Stephanie Teasley

Sociotechnical systems for collaboration and successful learning outcomes

Ambuj Tewari

Statistical methods for sequential decision making in personalized health

Michael Traugott

Mass media and their impact on American politics

Suleyman Uludag

Security, and optimization Smart Grid data, privacy

Shravan Veerapaneni

Fast and scalable algorithms for differential and integral equations on complex moving geometries

Nils G. Walter

Intracellular single molecule, high-resolution localization

Naisyin Wang

Models and methodologies for complex biomedical data

Kevin Ward

Simultaneous monitoring and processing of a large number of physiological parameters