Affiliated Faculty

520 MIDAS affiliate faculty members come from over 60 U-M departments, and include instructional (tenure / tenure track / lecturer), clinical and research track faculty.

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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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Allyson Flaster

Allyson Flaster

Nancy Fleischer

Survey methodology, epidemiology, health equity, policy, public health
Danny Forger

Danny Forger

Computer simulation and mathematical modeling of biological clocks

David Fouhey

Computer vision and machine learning

Edgar Franco-Vivanco

Analysis of historical data, Latin America, Natural language processing, criminal violence

Robert J. Franzese Jr.

Spatial-econometric methods for interdependent processes

Gary L. Freed

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

Vikram Gavini

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

Irina Gaynanova

Data integration and machine learning in biomedical applications
William J. Gehring

William J. Gehring

Anne Ruggles Gere

Deeper conceptual learning for students and enhanced pedagogy for faculty