Around 280 U-M faculty members from over 60 departments are affiliated with MIDAS.
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Brand and product portfolio management
Applied probability and statistics for stochastic processes
Scalable parallel algorithms for large scientific problems
Randomized approximation algorithms for massive data sets
Efficient management of a large number of devices through distributed intelligence
Stochastic modeling, decision and control theory and applications to networks
Statistical methodology for high-dimensional problems
Integrated Computational Materials Engineering (ICME), Materials Informatics and Quantum Computing.
Social factors of health behaviors and health outcomes
Statistical methodology for cancer research
Sociotechnical systems for collaboration and successful learning outcomes
computational population genetics
Statistical methods for sequential decision making in personalized health
collaborative use of computational systems for scholarly research, data curation, scientific data practices