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.
Contextually Private Mechanisms
A. Haupt‡, Z. Hitzig‡
Conditionally accepted at the American Economic Review
Machine Learning from Human Preferences
S. Truong, A. Haupt, S. Koyejo
Stanford Living Textbook initiative
Latent Adversarial Regularization for Offline Preference Optimization
E. Jiang, Y.J. Zhang, Y. Xu, A. Haupt, N. Amato, S. Koyejo
Preprint, 2026.
Algorithmic Risk Aversion and Recommendation-Mediated Demand
A. Haupt, A. Narayanan
Working Paper, 2026.
Optimal Aggregation Mechanisms for AI Benchmarking and Platinum Benchmarks
A. Haupt, A. Reuel, M. Kochenderfer, S. Koyejo
Working Paper.
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
Preprint, 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
Preprint, 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.
Preprint, 2025.
Non-Preferencing as a Fairness Provision: An Illustrated Guide
A. Haupt
SSRN Preprint 5338793, 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.
Platform Preferencing and Price Competition I: Evidence from Amazon
O. Hartzell‡, A. Haupt‡
SSRN Preprint 5126918, 2025.
The Economic Engineering of Personalized Experiences
A. Haupt
Ph.D. Dissertation, Massachusetts Institute of Technology, 2025.
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.
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.
Opaque Contracts
A. Haupt, Z. Hitzig
Preprint, 2023.
Recommending to Strategic Users
A. Haupt, D. Hadfield-Menell, C. Podimata
Preprint, 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.
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.
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.
Prior-Independent Auctions for the Demand Side of Federated Learning
A. Haupt, V. Mugunthan
ICLR Workshop on Distributed and Private Machine Learning, 2021.
Classification on Large Networks: A Quantitative Bound via Motifs and Graphons
A. Haupt, T. Schultz, M. Khatami, N. Tran
Advances in Mathematical Sciences: AWM Research Symposium, p. 107-126, 2020.
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
AI Safety and Content Moderation
Boundary guidance for filtered generation, cognitive safety, design for disobedience, quality control for generative models
Platforms and Competition
Self-preferencing fairness, optimal preferencing and price competition
Human Preferences in ML
Calibrated simulacra, contextual integrity of informed actions, preference measurement
AI Evaluation
Centaur benchmarks, LLM delusions evaluation, optimal benchmark aggregation
Labor and AI
A new O*Net, shift-share effects in language models
Networks and Information
How networks decay to homogeneity
Vita
Full Resume and CV are available as pdf.
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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 -
Lindau Nobel Laureate Meetings 2025Young Scientist
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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 -
Federal Trade Commission 2023Summer 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
Supervisor: B. Moldovanu -
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.