Affiliated Faculty

MIDAS works to foster interdisciplinary research collaboration across campus with our community of 550 affiliate faculty members, who come from over 60 U-M departments, and include instructional (tenure / tenure track / lecturer), clinical and research track faculty.


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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Naisyin Wang

Naisyin Wang

Models and methodologies for complex biomedical data
Runzi Wang

Runzi Wang

built environment, landscape architecture, machine learning, stormwater management, stream water quality, urban hydrology

Wenche Wang

two-sided markets and antitrust policy.

Wentao Wang

Civil and Environmental Engineering

Xu Wang

AI in Education, Data Science Education, Human-Computer Interaction, learning at scale

Xueding Wang

Medical imaging, biophotonics, medical ultrasound, robotics

Yixin Wang

Large-scale probabilistic machine learning, causal inference, machine learning for science
Kevin Ward

Kevin Ward

Simultaneous monitoring and processing of a large number of physiological parameters
Brian Weeks

Brian Weeks

Understanding biotic responses to global change
Lise Wei

Lise Wei

Bioinformatics and machine learning for outcome modeling
Joshua Welch

Joshua Welch

Machine learning for representing molecular cell states
Xiaoquan William Wen

Xiaoquan William Wen

Bayesian models and computation, Probabilistic graphical models
Brady West

Brady West

Measurement error in auxiliary variables and survey data

Jenna Wiens

Machine learning and data mining for healthcare data
David Williams

David Williams

Big Data and AI supported behavioral health care

Terrence Wong

clonal hematopoiesis, leukemia evolution