|Office:||5329 Sennott Square|
Dr. Hauskrecht 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 field of research interest is Artificial intelligence. However, he often works at the intersection of Computer Science with other fields, namely , probability theory, statistics, operations research, decision and control theory. His current research interests are in the areas of: Planning, reasoning ,and optimization in the presence of uncertainty; Machine learning and data mining; Applications of AI in medicine and finance.
The main objective of his research is to extend our abilitites to model and solve complex high-dimensional problems with stochastic components by developing: (1) new modeling frameworks allowing us to better represent special problem features and structure, and (2) efficient algorithmic solutions operating upon such model
Five Most 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.
- Artificial intelligence
- Planning and optimization in the presence of uncertainty
- Machine learning
- Applications of AI in medicine and investments