Margot Lisa-Jing Yann

Department of Computer Science
University of Toronto
email: lyan [at]

My research focuses on Artificial Intelligence, machine learining, and related applications in the bio-medical, economics fields, etc. Besides research, I also taught at the University of Toronto, St. George campus and Missisauga campus, as a sessional lecturer in 2015.

I worked on using machine learning methods to automate the high-throughput protein crystallization process during my postdoctoral research at the University Health Network. In 2013, I defended my PhD dissertation, titled "Multiagent Systems: Games and Learning from Structures", at the Lassonde School of Engineering of York University, under the supervision of professor Nick Cercone.

Research Experience

For my postdoctoral research, my main focus is to apply machine learning techniques (mainly deep convolutional neural networks), to analyze large-scale data in the bio-medical field and health care domain.

My PhD research lies in the areas of graphical model learning, multiagent learning, game theory, and reinforcement learning. In a multiagent environment, each agent acts according to its own goal while other agents are simultaneously making changes to the environment. My main interest is to explore how each agent can learn and adapt according to the behaviors of other agents in order to achieve optimal performance.

Learning Bayesian Networks is also a research interest of mine. It deals with learning probabilistic graphical models in order to represent and reveal the hidden causes of data. In addition, I have also worked on several research projects involving object recognition and visual attention modeling.

Teaching Experience

2015 Winter:


Multiagent Learning and Systems

Technical Report:

Yan, Lisa Jing. Thoughts on Multiagent Learning: From A Reinforcement Learning Perspective. York University, August 2010. [PDF][BibTex]


Yan, L. J., Cercone, N. (2011) Hierarchical adaptive cooperation for emergency response. First IEEE Canada Women in Engineering National Conference (IEEE WIENC). [Poster][BibTex]


Alfeld, S., Berkele, K., DeSalvo, S., Pham, T., Russo, D., Yan, L. J., and Taylor, M. E. (2011) Reducing the Team Uncertainty Penalty: Empirical and Theoretical Approaches. In AAMAS(2011) MSDM workshop. [PDF][BibTex]

Yan, L. J., Cercone, N. (2011) Bayesian Networks Learning for Strategies in Artificial Life. In N. Krasnogor & P. L. Lanzi (eds.), GECCO (Companion), Pages 821-822: ACM. ISBN: 978-1-4503-0690-4. [PDF][BibTex]

Bayesian Networks Learning


Yan, L. J., Cercone, N. (2010) Bayesian Networks Modeling for Evolutionary Genetic Structures.
Computer & Mathematics with Applications, Volume 59, Issue 8, Pages 2541-2551, ISSN 0898-1221. [PDF][BibTex]

Ji, J., Liu, C., Yan, J., Zhong, N. (2007) An improved Bayesian network structure learning algorithm and its application in an intelligent B2C portal.
Web Intelligence and Agent Systems. Vol. 5 No. 2: 127-138. IOS Press. [PDF]

Yan, J., Lv, S., Zhong, N. (2007) Artificial Life Modeling in Corporate Strategy.
Journal of Guangxi Normal University (Natural Science Edition). Vol. 25 No. 4:120-123.

Ji, J., Yan, J., Liu, C. (2006) An Improved Bayesian Networks Learning Algorithm Based on Independence Test and MDL-scoring.
Journal of Beijing University of Technology. Vol. 32 No. 5: 436-441.


Ji J., Yan, J., Liu, C., Zhong, N. (2005) An Improved Bayesian Networks Learning Algorithm Based on Independence Test and MDL Scoring.
Proc. IEEE International Conference on Active Media Technology, 315- 320. [PDF] [BibTex]

Ji, J., Liu, C., Yan, J., Zhong, N. (2004) Bayesian Networks Structure Learning and Its Application to Personalized Recommendation in a B2C Portal.
Proc. IEEE/WIC/ACM International Conference on Web intelligence. Web Intelligence. IEEE Computer Society, Washington, DC, 179-184. [PDF][BibTex]


Yan, L. J. (2010) Bayesian Networks Modeling for Evolutionary GeneticStructures.
Women in Machine Learning Workshop, Poster Presentation. [PDF]

Machine Learning & Bioinformatics


Li, J., Cercone, N., Wong, S.W.H., Yan, L.J. (2009) Enhancing Rule Importance Measure Using Concept Hierarchy.
The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), workshop on Quality issues, measures of interestingness and evaluation of data mining models (QIMIE). Bangkok, Thailand. April 27-30, 2009. [PDF]

Unpublished Technical Report:

Ph.D Dissertation: "Multiagent Systems: Games and Learning from Structures" 2013.

Master Thesis: "Bayesian Networks in Gene Selection" 2007.

Bachelor Thesis: "Learning Bayesian Network Structure: a Hybrid Algorithm" 2003.

Last Updated: September 15, 2015.