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

  1. 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.
  2. 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.
  3. 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])
  4. 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])
  5. 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])
  6. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. A Formal Separation Between Strategic and Nonstrategic Behavior. 
    James R. Wright and Kevin Leyton-Brown.
    EC-20: ACM Conference on Economics and Computation, 2020.
  6. 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.
  7. 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)
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. 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

  1. Bounds for Approximate Regret-Matching Algorithms. 
    Ryan D'Orazio, Dustin Morrill, James R. Wright.
    Bridging Game Theory and Deep Learning Workshop at NeurIPS, 2019.
  2. 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.
  3. 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.
  4. 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–
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 Examination Committees

2018
Moshe Mash (PhD, Ben Gurion)

Service

2020, 2021
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

2019, 2020
ACM Conference on Economics and Computation

Program Committees

2021
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, 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.

Teaching

Taught at University of Alberta

2021
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 04/2021