The MIDAS affiliate faculty community consists of over 450 U-M faculty members from over 60 departments.
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approximation algorithm, business analytics, machine learning, optimization
Data fusion for improving system operation and quality
Bayesian methoda and statistical modeling of biomedical data
Epidemiologic methods in chronic disease risk
Visualization and interpretation of massive data to monitor the Earth
computational social science and natural language processing
Analyzing failure time or event history data
Practical, accurate, and efficient methods for big data genome science
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
ICU Vital Sign for Opioid Use Prediction
Cancer treatment communication, and quality of care, decision-making
Longitudinal analyses across multiple databases, population modeling.
Single cell and spatiotemporal analyses of healthy and diseased tissues
Smart and adaptive water systems