Andreas Haupt
Human-Centered AI Fellow
Stanford Economics & Computer Science
Andreas Haupt is a Human-Centered AI Postdoctoral Fellow jointly appointed in Stanford’s Economics and Computer Science Departments, where he is advised by Erik Brynjolfsson and Sanmi Koyejo. He studies the elicitation and aggregation of human preferences in machine learning systems, including questions of privacy, competition, and consumer protection. He develops and applies methods of microeconomic theory, structural econometrics, and reinforcement learning to these domains. He earned a Ph.D. in Engineering-Economic Systems from MIT in February 2025 with a committee evenly split between Economics and Computer Science. Prior to that, he completed two master’s degrees at the University of Bonn—first in Mathematics (2017) and then in Economics (2018), with distinction. He has worked on competition enforcement for the European Commission’s Directorate-General for Competition and the U.S. Federal Trade Commission, and taught high school mathematics and computer science in Germany before his Ph.D. He remains committed to education and scholarship, most recently as a co-author of an upcoming textbook on Machine Learning from Human Preferences.
280-character bio
Andreas Haupt is a Human-Centered AI Postdoctoral Fellow at Stanford Economics and CS. He studies human preferences in ML, drawing on economics and RL. He earned his Ph.D. at MIT and has worked with the EU and FTC. Before academia, he taught high school math and CS in Germany.Tagline
Federal Trade Commission meets AI alignment.Publications
A more complete list of publications can be found on Google Scholar. ‡ indicates equal contribution or alphabetic author listing.
Optimal Aggregation Mechanisms for AI Benchmarking and Platinum Benchmarks
A. Haupt, A. Reuel, M. Kochenderfer, S. Koyejo
ICLR Workshop on AI for Mechanism Design and Strategic Decision Making, 2026.
The Foreman
G. Bellini, P. Ranganathan, A. Haupt
March 2026.
Latent Adversarial Regularization for Offline Preference Optimization
E. Jiang, Y.J. Zhang, Y. Xu, A. Haupt, N. Amato, S. Koyejo
Preprint, 2026.
Compressed Individuality
A. Haupt
39th Chaos Communication Congress; Heidelberg Laureate Forum, 2025.
Position: ML Conferences Should Establish a Refutations and Critiques Track
R. Schaeffer, J. Kazdan, Y. Denisov-Blanch, B. Miranda, M. Gerstgrasser, S. Zhang, A. Haupt, I. Gupta, E. Obbad, J. Dodge, et al.
Advances in Neural Information Processing Systems (Position Paper), 2025.
Those Who Control
A. Baradari, A. Haupt
November 2025.
Don't Walk the Line: Boundary Guidance for Filtered Generation
S. Ball, A. Haupt
Preprint, 2025.
Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion
A. Haupt
Allied Social Science Associations Meeting, 2025.
Scaling Human Judgment in Community Notes with LLMs
H. Li, S. De, M. Revel, A. Haupt, B. Miller, K. Coleman, J. Baxter, M. Saveski, M.A. Bakker
Journal of Online Trust and Safety 3 (1), 2025.
AI should not be an imitation game: Centaur evaluations
A. Haupt, E. Brynjolfsson
International Conference on Machine Learning (Position Paper), 2025.
Convex Markov Games: A Framework for Creativity, Imitation, Fairness, and Safety in Multiagent Learning
I. Gemp, A. Haupt, L. Marris, S. Liu, G. Piliouras
International Conference on Machine Learning, 2025.
Contextually Private Mechanisms
A. Haupt‡, Z. Hitzig‡
Forthcoming at the American Economic Review
Non-Preferencing as a Fairness Provision: An Illustrated Guide
A. Haupt
ACM Symposium on Computer Science and Law (Poster), 2025.
The Economic Engineering of Personalized Experiences
A. Haupt
Ph.D. Dissertation, Massachusetts Institute of Technology, 2025.
Platform Preferencing and Price Competition I: Evidence from Amazon
O. Hartzell‡, A. Haupt‡
SSRN Preprint 5126918, 2025.
Machine Learning from Human Preferences
S. Truong, A. Haupt, S. Koyejo
Stanford Living Textbook Initiative
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
B. Zhang, G. Farina, I. Anagnostides, F. Cacciamani, S. McAleer, A. Haupt, A. Celli, N. Gatti, V. Conitzer, T. Sandholm
Advances in Neural Information Processing Systems, 2024.
Risk Preferences of Learning Algorithms
A. Haupt‡, A. Narayanan‡
Games and Economic Behavior 148, p. 415-426, 2024.
Formal Contracts Mitigate Social Dilemmas in Multi-Agent Reinforcement Learning
A. Haupt‡, P. Christoffersen‡, M. Damani, D. Hadfield-Menell
Autonomous Agents and Multi-Agent Systems 38 (2), p. 1-38, 2024.
Black-Box Access is Insufficient for Rigorous AI Audits
S. Casper, C. Ezell, C. Siegmann, N. Kolt, T.L. Curtis, B. Bucknall, A. Haupt, K. Wei, J. Scheurer, M. Hobbhahn, et al.
ACM Conference on Fairness, Accountability, and Transparency, 2024.
Understanding Multi-Homing and Switching by Platform Drivers
X. Guo‡, A. Haupt‡, H. Wang, R. Qadri, J. Zhao
Transportation Research Part C: Emerging Technologies 154, 2023.
Steering No-Regret Learners to Optimal Equilibria
B.H. Zhang, G. Farina, I. Anagnostides, F. Cacciamani, S.M. McAleer, A. Haupt, A. Celli, N. Gatti, V. Conitzer, T. Sandholm
ACM Conference on Economics and Computation, 2023.
Certification Design for a Competitive Market
A. Haupt, N. Immorlica, B. Lucier
ACM Conference on Economics and Computation, 2023.
Recommending to Strategic Users
A. Haupt, D. Hadfield-Menell, C. Podimata
Symposium on the Foundations of Responsible Computing, 2023.
Opaque Contracts
A. Haupt, Z. Hitzig
Preprint, 2023.
Towards Psychologically-Grounded Dynamic Preference Models
M. Curmei‡, A. Haupt‡, B. Recht, D. Hadfield-Menell
ACM Conference on Recommender Systems, 2022.
The Optimality of Upgrade Pricing
D. Bergemann‡, A. Bonatti‡, A. Haupt‡, A. Smolin‡
Web and Internet Economics, 2021.
Multi-Agent Influence Diagrams and Commitment
A. Haupt
B.S. Thesis, Goethe Universität Frankfurt, 2019.
Voting with Restricted Communication
A. Haupt
M.S. Thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, 2018.
A Data Application of Graphon Theory
A. Haupt
M.S. Thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, 2017.
Die Integrality Ratio der Subtour-Relaxierung
A. Haupt
B.S. Thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, 2014.
Ongoing Interests
Preferencing & Persuasion
design for disobedience, non-self-preferencing for language models, cognitive safety
Privacy
roles in contextual integrity, contextual summaries
AI Evaluation
benchmark aggregation and certification, PastArena
New Ways to Learn about the Economy
A new O*Net, Centaur evaluations, foundation models for simulacra
Applied Theory of human-AI interaction
boundary guidance, concentration and preference measurement error, algorithmic risk aversion, network decay
Vita
Full Resume and CV are available as pdf.
-
Stanford University 2024-currentHuman-Centered AI Fellow
Departments of Computer Science and Economics
HAI, DEL, StatsML, CRFM, STAIR
Co-Chair, HAI Affinity Group on Global AI Governance -
MIT 2019-2024Ph.D. Student
Engineering-Economic Systems, Computer Science and AI Lab
Committee: A. Bonatti, D. Hadfield-Menell, E. Maskin, D. Parkes
Co-Chair of the AI Ethics and Policy Group
Vice-President of the Science Policy Initiative -
Summer Fellow
Office of International Affairs
Bureau for Competition -
European Commission Jan-May 2021Atypical Trainee
Directorate-General for Competition -
Berufliche Schulen Gross-Gerau 2017-19Teach First Deutschland Fellow
Full-time high school teaching -
University of Frankfurt 2017-19B.S. Student
Computer Science Department -
University of Bonn 2015-18M.S. Student (distinction)
Economics Department -
University of Bonn 2014-17M.S. Student
Mathematics Department
Year 1 at EPF Lausanne
Years 2-3 at Bonn -
University of Bonn 2012-14B.S. Student
Mathematics Department
h/t to Martin Saveski for inspiration and for a pointer to css code for the biographical timeline.