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Machine Learning

a)    Christos Davatzikos Computer-based image analysis methods, and their application to a wide variety of clinical research studies.

b)    Dean Foster Learning, hypothesis testing, variable selection.

c)    Shane Jensen Development of statistical methodology for a wide variety of application areas.

d)    Michael Kearns Applications of machine learning to finance, spoken dialogue systems, and other areas.

e)    Daniel Lee Learning representations that enable autonomous systems to efficiently reason about real-time behaviors.

f)    Elchanan Mossel Markov processes and Markov Random Fields.

g)   Aaron Roth Database privacy, game theory and mechanism design, and learning theory.

h)   Lyle Ungar Scalable machine learning and text mining methods, including clustering, feature selection, and semi-supervised and multi-task learning for natural language, psychology, and medical research.