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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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

Matthew Kay

Communicating uncertainty, and personal informatics, usable statistics
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
Branko Kerkez

Branko Kerkez

Smart and adaptive water systems

James Kibbie

Analysis of Bach's organ music and performance
John Kieffer

John Kieffer

Simulation-based predictive design for new materials

Hun-Seok Kim

and efficient VLSI architectures for low-power/high-performance wireless communication, and machine learning systems, computer vision, novel algorithms, signal processing, system analysis

Jinseok Kim

Text disambiguation in scholarly data
Minji Kim

Minji Kim

Computational 3D genomics

Aaron A. King

Statistical inference for mechanistic models of infectious disease