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Milos Hauskrecht
Milos Hauskrecht Professor

Biography

Dr. Hauskrecht is Professor of Computer Science, University of Pittsburgh. He received his Ph.D. from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in 1997. He received his M.Sc. in Electrical Engineering from the Slovak Technical University, Bratislava, Slovakia in 1988.

Dr. Hauskrecht's primary fields of research interest are Machine Learning and Artificial intelligence. His current research work explores new models and methods for time series analysis; low dimensional representations and summarizations of time series, structured active learning, planning and reinforcement learning. The majority of his research is driven by biomedical and clinical problems, especially problems related to analysis of high-dimensional Electronic Health Records (EHR) data and their application to adverse event prediction, medical error detection, monitoring and alerting. Dr. Hauskrecht and his group  have developed and keep expanding a new data-centric platform supporting the development and deployment of machine learning solutions to real-time EHR workflows and real-time monitoring andalerting in which hundreds of machine learning models are deployed in real time over a wide range of data sources and data types present in todays EHRs.

Research Interests

Machine learning
Artificial Intelligence
Time-series modeling and analysis
Electronic Health Record (EHR) data modeling
Clinical monitoring and alerting

Recent Publications

C. Hong, I. Batal, and M. Hauskrecht, "A Generalized Mixture Framework for Multi-label Classification," SIAM Data Ming Conference, pp. 712-720, Vancouver, Canada, April 2015.

Z. Liu and M. Hauskrecht, "Clinical Time Series Prediction: Toward a Hierarchical Dynamical System Framework," Artificial Intelligence in Medicine, , pp. 5-18, 2015.

I. Batal, G. Cooper, D. Fradkin, J. Harrison, F. Moerchen, and M. Hauskrecht, "An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Date," Knowledge and Information Science Journal, pp. 1-36, 2015.

E. Heim, M. Berger, L. Seversky, and M. Hauskrecht, "Efficient Online Relative Comparison Kernel Learning," SIAM Data Mining Conference, pp. 271-279, Vancouver, Canada, April 2015.

Z. Liu and M. Hauskrecht, "A Regularized Linear Dynamical System Framework for Multi-variate Time Series Analysis," The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), pp. 1498-1904, Austin, TX, January 2015.