The MIDAS affiliate faculty community consists of over 420 U-M faculty members from over 60 departments.


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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Niko Kaciroti

Bayesian Modeling

Jack D. Kalbfleisch

Analyzing failure time or event history data

Hyun Min Kang

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

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

Steven J. Katz

Cancer treatment communication, and quality of care, decision-making

Matthew Kay

Communicating uncertainty, and personal informatics, usable statistics

Daniel P. Keating

Longitudinal analyses across multiple databases, population modeling.

Evan Keller

Single cell and spatiotemporal analyses of healthy and diseased tissues

Branko Kerkez

Smart and adaptive water systems

Marouane Kessentini

Search-based software engineering and software refactoring

James Kibbie

Analysis of Bach's organ music and performance

John Kieffer

Simulation-based predictive design for new materials

Jinseok Kim

Text disambiguation in scholarly data

Aaron A. King

Statistical inference for mechanistic models of infectious disease

Ken Kollman

Political science, data infrastructure, elections, organizations