Assistant Professor
Joined School 2011
PhD (University of Alberta),
MSc (University of Alberta),
BCS (University of New Brunswick)
| dlizotte@uwaterloo.ca | |
| Web | http://www.cs.uwaterloo.ca/~dlizotte/ |
| Voice | 519-888-4567 x34469 |
| Fax | 519-885-1208 |
|
| Daniel Lizotte
Assistant Professor Joined School 2011 PhD (University of Alberta),
|
Professor Lizotte is interested in the areas of machine learning, reinforcement learning, and statistics, particularly as they apply to problems in health informatics. We are now seeing the development of electronic data sources that record how thousands or even millions of patients respond to different sequences of treatments over time, and these have the potential to inform evidence-based non-myopic medical decision making more effectively than previous studies. However current techniques are not always well-suited to this task. Professor Lizotte's basic research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques so they can be applied to these new sources of sequential medical data, and can in turn provide doctors with the best available evidence for non-myopic decision making.
D. J. Lizotte. Convergent Fitted Value Iteration with Linear Function Approximation. To appear in Neural Information Processing Systems 25, 2011.
S. Shortreed, E. B. Laber, D. J. Lizotte, T. S. Stroup, J. Pineau, and S. A. Murphy. Informing sequential clinical decision-making through reinforcement learning: an empirical study. Machine Learning, 84:109–136, 2011. DOI: 10.1007/s10994-010-5229-0.
D. J. Lizotte, R. Greiner, and D. Schuurmans. An experimental methodology for response surface optimization methods. Journal of Global Optimization, pages 1–38, 2011. Online First: 10.1007/s10898-011-9732-z.
D. J. Lizotte, M. Bowling, and S. A. Murphy. Efficient reinforcement learning with multiple reward functions for randomized clinical trial analysis. In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML), 2010.
D. J. Lizotte, T. Wang, M. Bowling, and D. Schuurmans. Automatic gait optimization with Gaussian process regression. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007.
David R. Cheriton School of Computer Science
University of Waterloo
Waterloo, Ontario, Canada N2L 3G1
Tel: 519-888-4567 x33293
Fax: 519-885-1208
Contact | Feedback: cs-webmaster@cs.uwaterloo.ca | David R. Cheriton School of Computer Science | Faculty of Mathematics
Last modified: Friday, 28-Oct-2011 10:50:10 EDT
