Dan Lizotte, PhD

Dr. Lizotte

Assistant Professor

P:519.661.2111 ext: 86644


Research Cluster Membership

Research Interests

  • Professor Lizotte's research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques so they can be applied to new sources of medical data, and can in turn provide clinicians with the best available evidence for non-myopic medical decision making. He is particularly interested in problems involving multiple outcomes, causal inference, and outlier detection.


  • BCS (New Brunswick)
  • MSc, PhD in Computer Science (Alberta)

Recent Research Grants

  • Machine learning for non-myopic decision support and knowledge discovery (NSERC; 2012-2017)

 Publications (selected) 

  • Eric B. Laber,Daniel J. Lizotte, Min Qian, and Susan A. Murphy. Dynamic treatment regimes: technical challenges and applications. Electronic Journal of Statistics, 8(0):1225–1272, 2014.
  • Eric B. Laber, Daniel J. Lizotte, and Bradley Ferguson. Set-valued dynamic treatment regimes for competing outcomes. Biometrics, 70(1):53–61, March 2014
  • Daniel J. Lizotte, Michael Bowling, and Susan A. Murphy. Linear fitted-Q iteration with multiple reward functions. Journal of Machine Learning Research, 13:3253–3295, Nov 2012. 
  • Daniel J. Lizotte. Convergent fitted value iteration with linear function approximation. In J. Shawe-Taylor, R.S. Zemel, P. Bartlett, F.C.N. Pereira, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2537– 2545. NIPS Foundation, 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.