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Statistical Modeling and Learning

Statistical modeling and learning use statistical models and assumptions to translate complex real-world problems into tractable structures so that predictions about uncertain outcomes or prescriptions to design systems can be made. Going from data to models, our methodological research in this area is shaped around devising novel frameworks that can lead to fair and interpretable decisions and insights. Key drivers to our fundamental research in this area are the explosion in the availability of data and computational powers in the last decade 

This area includes:     

  • Reinforcement Learning
  • Machine Learning
  • Applied Probability
  • Data-driven human performance and behavior modeling; digital (wearable) monitoring and analytics
  • Statistical Modeling

Faculty in this Area:

Jackie Cha, David NeyensHamed Rahimian, Kevin M. Taaffe, Dan Li





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