James R. Wright

Assistant Professor
Department of Computing Science
University of Alberta
Edmonton, Alberta
james.wright@ualberta.ca

Academic Employment

2018–present: Assistant Professor
Department of Computing Science
University of Alberta, Edmonton, Alberta.
2016–2018: Postdoctoral Researcher
Microsoft Research, New York, NY.
2015: Visiting Graduate Student
Simons Institute for the Theory of Computing
University of California, Berkeley, CA.

Education

2010–2016: Doctor of Philosophy (Computer Science)
Dissertation: Modeling Human Behavior in Strategic Settings.
ACM SIGecom Doctoral Dissertation Award (Honorable Mention)
University of British Columbia, Canada
2007–2010: Master of Science (Computer Science)
Thesis: Beyond Equilibrium: Predicting Human Behaviour in Normal Form Games
University of British Columbia, Canada
1995–2000: Bachelor of Science (Computing Science)
Simon Fraser University, Canada

Publications

Journals

Why Do Software Developers Use Static Analysis Tools? A User-Centered Study of Developer Needs and Motivations.
Lisa Nguyen Quang Do, James R. Wright, and Karim Ali.
IEEE Transactions on Software Engineering, 2020.
How can machine learning aid behavioral marketing research?
Linda Hagen, Kosuke Uetake, Nathan Yang, Bryan Bollinger, Allison J. B. Chaney, Daria Dzyabura, Jordan Etkin, Avi Goldfarb, Liu Liu, K. Sudhir, Yanwen Wang, James R. Wright, and Ying Zhu.
Marketing Letters, 2020.
Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth
Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown.
Artificial Intelligence 275, 2019.
(supersedes Gao, Wright, and Leyton-Brown [2016])
Learning When to Stop Searching.
Daniel G. Goldstein, R. Preston McAfee, Siddarth Suri, and James R. Wright.
Management Science 66:3, pages 1375–1394, March 2020.
(Full version of Goldstein et al. [2017])
Level-0 Models for Predicting Human Behavior in Games.
James R. Wright and Kevin Leyton-Brown.
Journal of Artificial Intelligence Research, Volume 64, pages 357–383, February 2019.
(supersedes Wright & Leyton-Brown [2014])
Predicting Human Behavior in Unrepeated, Simultaneous-Move Games.
James R. Wright and Kevin Leyton-Brown.
Games and Economic Behavior, Volume 106, pages 16–37, November 2017.
(supersedes Wright & Leyton-Brown [2010, 2012])

Peer-Reviewed Conferences

The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models.
Greg d'Eon, Jason d'Eon, James R. Wright, and Kevin Leyton-Brown.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022.
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games.
Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright, Michael Bowling, and Amy Greenwald.
ICML 2021: International Conference on Machine Learning, 2021.
The Role of Accuracy in Algorithmic Process Fairness Across Multiple Domains.
Michele Albach and James R. Wright.
EC-21: ACM Conference on Economics and Computation, 2021.
Hindsight and Sequential Rationality of Correlated Play.
Dustin Morrill, Ryan D'Orazio, Reca Sarfati, Marc Lanctot, James R. Wright, Amy Greenwald, and Michael Bowling.
AAAI 2021: AAAI Conference on Artificial Intelligence, 2021.
Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth (Extended Abstract).
Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown.
IJCAI-PRICAI 2020 Journal Track, 2020.
A Formal Separation Between Strategic and Nonstrategic Behavior.
James R. Wright and Kevin Leyton-Brown.
EC-20: ACM Conference on Economics and Computation, 2020.
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization.
Ryan D'Orazio, Dustin Morrill, James R. Wright, and Michael Bowling.
AAMAS 2020: International Conference on Autonomous Agents and Multiagent Systems, 2020.
Learning in the Repeated Secretary Problem.
Daniel G. Goldstein, R. Preston McAfee, Siddarth Suri, and James R. Wright.
EC-17: ACM Conference on Economics and Computation, 2017.
(Abstract)
Deep Learning for Predicting Human Strategic Behavior.
Jason Hartford, James R. Wright, and Kevin Leyton-Brown.
NIPS 2016: Annual Conference on Neural Information Processing Systems, 2016.
Oral presentation.
Mechanical TA: Partially Automated High-Stakes Peer Grading.
James R. Wright, Chris Thornton, and Kevin Leyton-Brown.
SIGCSE-15: ACM Technical Symposium on Computer Science Education,
pages 96–101, 2015.
Level-0 Meta-Models for Predicting Human Behavior in Games.
James R. Wright and Kevin Leyton-Brown.
EC-14: ACM Conference on Economics and Computation, pages 857–874, 2014.
Behavioral Game-Theoretic Models: A Bayesian Framework For Parameter Analysis.
James R. Wright and Kevin Leyton-Brown.
AAMAS-2012: International Conference on Autonomous Agents and Multiagent Systems, pages 921–928, 2012.
Best student paper (runner up).
Beyond Equilibrium: Predicting Human Behavior in Normal Form Games.
James R. Wright and Kevin Leyton-Brown.
AAAI-10: AAAI Conference on Artificial Intelligence, pages 901–907, 2010.

Other Venues

Bounds for Approximate Regret-Matching Algorithms.
Ryan D'Orazio, Dustin Morrill, James R. Wright.
Bridging Game Theory and Deep Learning Workshop at NeurIPS, 2019.
A Formal Separation Between Strategic and Nonstrategic Behavior.
James R. Wright and Kevin Leyton-Brown.
Workshop on Behavioral EC at ACM Conference on Economics and Computation, 2019.
Incentivizing Evaluation via Limited Access to Ground Truth:
Peer-Prediction Makes Things Worse.
Xi Alice Gao, James R. Wright, and Kevin Leyton-Brown.
Workshop on Algorithmic Game Theory and Data Science at ACM Conference on Economics and Computation, 2016.
Linear Solvers for Nonlinear Games: Using Pivoting Algorithms to Find Nash Equilibria in n-Player Games.
James R. Wright, Albert Xin Jiang, and Kevin Leyton-Brown.
SIGecom Exchanges, volume 10, number 1, pages 9–12, 2011.

Invited Talks

PKU: A Formal Separation Between Strategic and Nonstrategic Behavior.
Peking University.
Beijing, China (virtual talk). 2020.
DLRLSS-2019: Multiagent Systems.
At Deep Learning & Reinforcement Learning Summer School.
Edmonton, Alberta. 2019.
Choice-2019: Algorithmic Behavioral Modeling.
At 11th Triennial Invitational Choice Symposium.
Chesapeake Bay, Maryland. 2019.
PGT-2018: Predicting Human Strategic Behavior: From Behavioral Economics to Deep Learning.
At Workshop on Predictive Game Theory,
Evanston, Illinois. 2018.
YoungEC'17: Algorithmic Modeling of Human Behavior.
At Young Researcher Workshop on Economics and Computation,
Tel Aviv, Israel. 2017.
INFORMS-2017: Deep Learning for Human Strategic Modeling.
At INFORMS Annual Meeting,
Houston, Texas. 2017.
IFORS-2017: Deep Learning for Human Strategic Modeling.
At 21st Conference of the International Federation of Operations Research Societies,
Qu\'{e}bec City, Qu\'{e}bec. 2017.
ISMP-2015: Level-0 Meta-Models for Predicting Human Behavior in Games. At 22nd International Symposium on Mathematical Programming,
Pittsburgh, Pennsylvania. 2015.
SFI: Evaluating Set-Valued Predictions.
At Combining Information Theory and Game Theory,
Santa Fe Institute, New Mexico. 2012.
LANL: Beyond Equilibrium: Predicting Human Behavior in Normal Form Games.
At Design and Control of Systems of Goal-Directed Agents; From Game Theory to Game Engineering,
Los Alamos National Laboratory, New Mexico. 2010.
BQGT: Beyond Equilibrium: Predicting Human Behavior in Normal Form Games.
At Behavioral and Quantitative Game Theory Conference on Future Directions,
Newport Beach, California. 2010.

Funding

2019–2025: NSERC Discovery Grant
Natural Sciences and Engineering Research Council of Canada
(Total value: $195,000)
2019–2020: NSERC Discovery Launch Supplement
Natural Sciences and Engineering Research Council of Canada
(Total value: $12,500)
2019–2020: Amii Resource Allocation Panel
Alberta Machine Intelligence Institute
(Total value: $78,860)
2018–2023: Canada CIFAR AI Chair
Canadian Institute for Advanced Research
(Total value: $500,000)
2018: NVIDIA GPU Grant
NVIDIA Corporation (Donation value in CAD: $1,512)

Awards

2017: Honorable Mention: ACM SIGecom Doctoral Dissertation Award
ACM Special Interest Group on E-commerce
2010–2013: UGF: University Graduate Fellowship
University of British Columbia, Canada
Declined in 2010–2012 to hold NSERC
(Total value: $80,000)
2010–2012: NSERC Canada Graduate Scholarship (Ph.D.)
Natural Sciences and Engineering Research Council of Canada
(Total value: $105,000)
2008–2009: NSERC Canada Graduate Scholarship (M.Sc.)
Natural Sciences and Engineering Research Council of Canada
(Total value: $17,500)

Graduate Students Supervised

2021–: Rohini Das (MSc, co-supervised with Neil Burch)
2021–: Revan MacQueen (MSc)
2020–: Amirmohsen Sattarifard (MSc, co-supervised with Matt Taylor)
2020–2022: Shehroze Khan (MSc)
2020–: Niko Yasui (PhD)
2019–: Greg d'Eon (PhD, co-supervised with Kevin Leyton-Brown)
2019–2021: Michele Albach (MSc)
2019–: Daniel Chui (MSc)
2019–2020: Ryan D'Orazio (MSc, co-supervised with Matt Taylor, now at MILA)

Supervisory / Examination Committees

2021–: Kristen Yu (PhD)
2021–: Housam Babiker (PhD)
2020: Varun Bhatt (MSc)
2020: Sam Sokota (MSc)
2020: Niko Yasui (MSc)
2019–: Trevor Davis (PhD)
2019–: Adam Parker (PhD)
2019–: Negar Hassanpour (PhD)
2019: Wesley Chung (MSc)
2019: Md Solimul Chowdhury (PhD)
2018–2020: Craig Sherstan (PhD)
2018: Marius Stanescu (PhD)

External Examiner

2022: Atrisha Sarkar (PhD, Waterloo University)
2022: David Milec (PhD proposal, Czech Technical University)
2018: Moshe Mash (PhD, Ben-Gurion University)

Service

2020–2023: Amii Resource Allocation Panel (co-chair starting 2022)
2022: Graduate Admissions Strategy Committee
2020–2022: Graduate Admissions Committee
2020: Program Co-chair: Graduate Student Symposium (at 33rd Canadian Conference on Artificial Intelligence)
2019: Program Committee: Deep Learning & Reinforcement Learning Summer School
2017: Co-organizer: 2017 New York Computer Science and Economics Day (NYCE Day)
2015–2017: Member: NSF PI Forum on Peer Assessment
2014–2015: Student representative: Faculty Recruiting Committee

Senior Program Committees / Area Chair

2022: AAAI Conference on Artificial Intelligence.
2019, 2020, 2022: ACM Conference on Economics and Computation.

Program Committees

2021, 2022: Equity and Access in Algorithms, Mechanisms, and Optimization.
2017, 2020: International Conference on Autonomous Agents and Multi-Agent Systems.
2019: International Conference on Machine Learning.
2019–2020: AAAI Conference on Human Computation and Crowdsourcing.
2018–2020: AAAI Workshop on Reinforcement Learning in Games.
2017–2021: The Web Conference (formerly International World Wide Web Conference).
2017, 2018, 2021: ACM Conference on Economics and Computation.
2016–2021: AAAI Conference on Artificial Intelligence.

Journal Reviews

I have reviewed for various journals without serving on an editorial board. These include American Economic Review, Artificial Intelligence Journal, Journal of Artificial Intelligence Research, Journal of Autonomous Agents and Multi-Agent Systems, Econometrica, Journal of Economic Behavior and Organization, Journal of Economic Theory, Games and Economic Behavior, Progress in Artificial Intelligence, Journal of Machine Learning Research, and ACM Transactions on Economics and Computation.

Conference Reviews

I have reviewed for various conferences without serving on a program committee. These include NeurIPS, SODA, WINE, IJCAI, ICLR.

Teaching

Taught at University of Alberta

2022: CMPUT 654: Modelling Human Strategic Behaviour (graduate, co-taught with Kevin Leyton-Brown at UBC)
2022: CMPUT 366: Intelligent Systems
2021 (twice): CMPUT 455: Search, Knowledge, and Simulations
2021: CMPUT 366: Intelligent Systems
2020: CMPUT 296: Basics of Machine Learning
2020 (twice): CMPUT 366: Intelligent Systems
2020: CMPUT 654: Modelling Human Strategic Behaviour (graduate)
2019: CMPUT 654: Modelling Human Strategic Behaviour (graduate)
2019: CMPUT 366: Intelligent Systems

Other Courses

My duties as an instructional assistant for the various massively open online courses listed below included constructing new content (problem sets and exams), cross-checking new video content for slide typos and misstatements, and monitoring and responding to student questions in online forums.

As an instructional assistant for Computers and Society, I led the design and implementation effort of the Mechanical TA peer grading system. I also constructed exams, and assisted with curriculum development.

As a teaching assistant for Multiagent Systems, I constructed quizzes, exams, and assignments, and assisted in the day-to-day operation of the class.

2014: Game Theory II (Massively Open Online Course)
Instructional Assistant, Coursera/University of British Columbia
2014: Game Theory (Massively Open Online Course)
Instructional Assistant, Coursera/University of British Columbia
2014: CPSC 532L: Multiagent Systems (graduate)
Teaching Assistant, University of British Columbia
2014: CPSC 430: Computers and Society
Instructional Assistant, University of British Columbia
2013: Game Theory II (Massively Open Online Course)
Instructional Assistant, University of British Columbia
2013 (twice): Game Theory (Massively Open Online Course)
Instructional Assistant, Coursera/University of British Columbia
2013: CPSC 532L: Multiagent Systems (graduates)
Teaching Assistant, University of British Columbia
2013: CPSC 430: Computers and Society
Instructional Assistant, University of British Columbia
2009: CPSC 532L: Multiagent Systems (graduates)
Teaching Assistant, University of British Columbia
2008: CPSC 430: Computers and Society
Teaching Assistant, University of British Columbia
2007: CPSC 410: Advanced Software Engineering
Teaching Assistant, University of British Columbia

Last update: Aug 02/2022