The MIDAS affiliate faculty community consists of >400 U-M faculty members from over 60 departments.

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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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Deanna Isaman

chronic disease, data synthesis, economic model

Christiane Jablonowski

Physics-guided machine learning, climate modeling, emulation

Abigail Jacobs

Computational social science, empirical design, governance, observational data, social networks, statistical inference

H. V. Jagadish

Database systems, query models and analytics processes for reliable insight

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

Matthew Johnson-Roberson

Visualization and interpretation of massive data to monitor the Earth

Joyce Penner


David Jurgens

computational social science and natural language processing

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