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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Robert J. Franzese Jr.

Spatial-econometric methods for interdependent processes

Gary L. Freed

Immunization policy, health policy and health economics for children

Ayumi Fujisaki-Manome

Arctic Ocean, Great Lakes, Sea/lake ice, hydrodynamics, machine learning, numerical modeling
Johann Gagnon-Bartsch

Johann Gagnon-Bartsch

High-throughput and high-dimensional data analysis
Andrzej T Galecki

Andrzej T Galecki

Computational methods for correlated and over-dispersed data
Krishna Garikipati

Krishna Garikipati

Scientific machine learning, data-driven modeling
Lana Garmire

Lana Garmire

Applying data science for actionable transformation of human health

Vikram Gavini

Ab-initio calculations, DFT, electronic structure, exchange-correlation approximation, numerical methods

Irina Gaynanova

Data integration and machine learning in biomedical applications

Anne Ruggles Gere

Deeper conceptual learning for students and enhanced pedagogy for faculty

Maani Ghaffari

Autonomous Navigation, Equivariant Representation Learning, Geometric Estimation and Control, Robot Learning and Control, Robot Perception

Hamid Ghanbari

digital health solutions for atrial fibrillation

Eric Gilbert

social computing and social media research
Brenda Gillespie

Brenda Gillespie

Censored data and clinical trials

Pamela Giustinelli

The interplay of brain, and behavior during human development, biology

Sharon Glotzer

Computer simulation of nanosystems' self assembly