I am an Assistant Professor, Teaching Stream in Computer Science at the University of Toronto. Before this, I was a lecturer and advisor in Computer Science at the University of Waterloo. I obtained my Ph.D. in Computer Science from Harvard University advised by Yiling Chen, and then was a postdoc working with Kevin Leyton-Brown at the University of British Columbia. Before Harvard, I was an undergraduate student in Computer Science and Mathematics at University of British Columbia.

Teaching

Courses at the University of Toronto:

• CSC 384. Introduction to Artificial Intelligence. Winter 2023
• CSC 384. Introduction to Artificial Intelligence. Fall 2022
• CSC 311. Introduction to Machine Learning. Fall 2022

Courses at the University of Waterloo:

• CS 486/686. Introduction to Artificial Intelligence. Fall 2021


• CS 486/686. Introduction to Artificial Intelligence. Spring 2021
• CS 486/686. Introduction to Artificial Intelligence. Winter 2021
• CS 486/686. Introduction to Artificial Intelligence. Fall 2020


• CS 486/686. Introduction to Artificial Intelligence. Spring 2020
• CS 245. Logic and Computation. Fall 2019


• CS 486/686. Introduction to Artificial Intelligence. Spring 2019
• CS 136. Elementary Algorithm Design and Data Abstraction. Winter 2019
• CS 486/686. Introduction to Artificial Intelligence. Fall 2018


• CS 245. Logic and Computation. Spring 2018
• CS 136. Elementary Algorithm Design and Data Abstraction. Winter 2018
• CS 245. Logic and Computation. Fall 2017

Courses at the University of British Columbia:

• CPSC 121. Models of Computation. Spring 2017
• CPSC 121. Models of Computation. Fall 2016
• CPSC 121. Models of Computation. Spring 2017

Research

At present, I work on education research. My past research was broadly at the intersection of artificial intelligence and game theory. I think about how to design the incentives in a system to get accurate information from strategic participants. My work has tackled a range of problems including designing grading mechanisms and forecasting future events, by using a mix of theoretical and experimental methods.

Publications

• COVID-19, Students and the New Educational Landscape. (Extended Abstract)
  Angela A. Siegel, Mark Zarb, Emma Anderson, Brent Crane, Alice Gao, Celine Latulipe, Ellie Lovellette, Fiona McNeill, and Debbie Meharg.
  In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2 (ITiCSE 2022).
  Association for Computing Machinery, New York, NY, USA, 574–575.
  
  
• Incentivizing Evaluation via Limited Access to Ground Truth: Peer-Prediction Makes Things Worse
  Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown
  Artificial Intelligence Journal (AIJ), volume 275, pp. 618-638, October 2019.
  
  
• Trick or treat: putting peer prediction to the test.
  Xi Alice Gao, Andrew Mao, Yiling Chen, and Ryan Prescott Adams.
  In Proceedings of the fifteenth ACM conference on Economics and computation (EC 2014).
  Association for Computing Machinery, New York, NY, USA, 507–524.
  
  
• Market manipulation with outside incentives.
  Chen, Y., Gao, Xi Alice, Goldstein, R.
  Auton Agent Multi-Agent Syst 29, 230–265 (2015).
  (Supersedes the AAAI 2011 paper below.)
  
  
• What you jointly know determines how you act: strategic interactions in prediction markets.
  Xi Alice Gao, Jie Zhang, and Yiling Chen.
  In Proceedings of the fourteenth ACM conference on Electronic commerce (EC '13).
  Association for Computing Machinery, New York, NY, USA, 489–506.
  
  
• Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests
  Xi Alice Gao, Yoram Bachrach, Peter Key, and Thore Graepel.
  In Proceedings of the 26th Conference on Artificial Intelligence (AAAI 2012), Toronto, ON, Canada, 2012.
  
  
• Adaptive Polling for Information Aggregation
  Thomas Pfeiffer, Xi Alice Gao, Andrew Mao, Yiling Chen, and David G. Rand.
  In Proceedings of the 26th Conference on Artificial Intelligence (AAAI 2012), Toronto, ON, Canada, 2012.
  
  
• Market Manipulation with Outside Incentives
  Yiling Chen, Xi Alice Gao, Rick Goldstein, and Ian A. Kash.
  In Proceedings of the 25th Conference on Artificial Intelligence (AAAI 2011), San Francisco, CA, 2011.
  
  
• An Axiomatic Characterization of Continuous-Outcome Market Makers
  Xi Alice Gao and Yiling Chen.
  In Proceedings of the 6th Workshop on Internet and Network Economics (WINE 2010), Stanford, CA, 2010.
  
  
• Learning Game Representations from Data Using Rationality Constraints
  Xi Alice Gao and Avi Pfeffer.
  In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, CA, 2010.
  
  
• Betting on the Real Line
  Xi Alice Gao, Yiling Chen, and David M. Pennock.
  In Proceedings of the 5th Workshop on Internet and Network Economics (WINE 2009), Rome, Italy, 2009.
  

Ph.D. Dissertation

Eliciting and Aggregating Truthful and Noisy Information
School of Engineering and Applied Sciences, Harvard University, September 2014.

SIGecom Doctoral Dissertation Award runner up
IFAAMAS Victor Lesser Distinguished Dissertation Award runner up