Affiliated Faculty

500 MIDAS affiliate faculty members 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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Judy Jin

Data fusion for improving system operation and quality

Timothy D. Johnson

Bayesian methoda and statistical modeling of biomedical data

Vicki Johnson-Lawrence

Epidemiologic methods in chronic disease risk

David Jurgens

computational social science and natural language processing
Niko Kaciroti

Niko Kaciroti

Bayesian Modeling, dynamic models.
Jack D. Kalbfleisch

Jack D. Kalbfleisch

Analyzing failure time or event history data
Vineet Kamat

Vineet Kamat

Construction; Robotics; Human-Robot Collaboration

Hyun Min Kang

Practical, accurate, and efficient methods for big data genome science
Jian Kang

Jian Kang

Bayesian methods, composite likelihood approach and missing data problems, efficient statistical computation algorithms, graphical models, latent source separation methods, network inference, ultrahigh-dimensional feature selection

Trishul Kapoor

ICU Vital Sign for Opioid Use Prediction

Steven J. Katz

Cancer treatment communication, and quality of care, decision-making
Daniel P. Keating

Daniel P. Keating

Longitudinal analyses across multiple databases, population modeling.

Evan Keller

Single cell and spatiotemporal analyses of healthy and diseased tissues