Sarah H. Cen

I am a postdoc at Stanford HAI, fortunate enough to be working with Prof. Percy Liang in the Department of Computer Science and Prof. Daniel E. Ho in the Stanford Law School's RegLab. I'm also an incoming Assistant Professor at CMU in the Departments of Electrical & Computer Engineering and Engineering & Public Policy.

My research falls at the intersection of machine learning and AI accountability. I'm interested in both the design and governance of AI and automated systems. On the design side, I study how to better devise algorithms to work when interfacing with humans. I enjoy using a wide range of methods to tackle research questions, including tools from ML, statistical learning, causal inference, and game theory. On the governance side, I study law and policy to improve our understanding of how our legal system and regulatory frameworks can adapt in an age of AI and automation.

Recently, I have written on the regulation and auditing of social media algorithms; the emergence of AI supply chains; the estimation of counterfactual potential outcomes under spillover effects; how competing for resources under uncertainty affects long-term outcomes; and individual-level rights of data-driven decision subjects.

I completed my PhD at MIT in EECS, under the wonderful mentorship of Prof. Aleksander Mądry and Prof. Devavrat Shah. Previously, I worked in intelligent transportation, communication networks, reinforcement learning, and robotics. During my master's, I performed research on autonomous vehicles (a.k.a. self-driving cars) with Prof. Paul Newman at the University of Oxford. As an undergraduate, I studied control & decision systems with Prof. Naomi Leonard at Princeton University.

Please find my (to-be-updated) C.V. here.

Selected Recent Talks

• Adapting for AI - slides
  Presented at Columbia's Frontiers of ML Seminar and Northeastern's Network Science Institute (February 2025).
• Access and Evidence in AI Auditing - slides
  Presented at EAAMO'24 (October 2024).
• ⭐ My Thesis: Paths to AI Accountability Through Design, Measurement, and Regulation - slides
  Gave my thesis defense August 2024.
• A Game-Theoretic Perspective on Trustworthy Data-Driven Algorithms - slides
  Presented at 2024 Conference on Economics and Computation (EC'24), and at the 2023 INFORMS Annual Meeting in the Session on Fairness in Platforms and Recommendations.
• Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference - slides
  Presented in the Session on Decisions Under Not-So-Perfect Data at the 2023 INFORMS Annual Meeting.
• Design and Governance of Data-Driven Algorithms - slides
  Presented at the Cornell Young Researchers Workshop (October 2023).
• The Right to be an Exception to a Data-Driven Rule - manuscript | slides
  Presented at the Privacy Law Scholars Conference (June 2022), the Harvard Ethical Issues in Computing & AI Conference (June 2022), and the MIT AI Ethics Reading Group.
• Regulating Algorithmic Filtering on Social Media - manuscript | slides | poster
  Presented as a Spotlight at NeurIPS (December 2021), LIDS Student Conference (January 2022) and received Best Student Presentation, LIDS Stats & Tea Talk (April 2021), and Stanford GSB Rising Scholars Conference (October 2020).
• Regret, stability, and fairness in matching markets with bandit learners - manuscript | slides | poster
  Presented as poster at AISTATS (March 2022) and as a LIDS Stats & Tea Talk (May 2021).
• A Game-Theoretic Perspective on Trust in Recommendation - slides
  Presented as a Contributed Talk in the ICML Workshop on Responsible Decision Making in Dynamic Environments (July 2022) and at the Simon's Institute for Theory of Computing's Workshop on AI & Humanity (July 2022).
• Why Social Media Platforms Shape What You Think - slides
  Presented at INFORMS (October 2022).
• Making Online Platforms Fit For Humans
  Presented as a Thriving Star of AI at MIT (May 2022).

Education

• Inference & Information  |
• Probability Theory  |
• Algorithms for Inference  |
• Linear Optimization  |
• Mathematical Statistics  |
• Discrete Probability & Stochastic Processes  |
• Individual Risk  |
• Artificial Intelligence  |
• Microeconomic Theory  |
• Game Theory  |
• Automatic Control Systems  |
• Big Data  |
• Ethics & Fairness in Data-Driven Decision-Making  |
• Law & Ethics of AI  |
• Urban Sociology  |
• Race & Ethnicity  |
• History of Poverty  |
• Political Theory  |
• Human Rights

Research

To see my research experiences, click here.

Publications and Pre-prints

*First-author contribution.

1. S. H. Cen*, A. Ilyas, H. Driss, C. Park, A. K. Hopkins, C. Podimata, and A. Mądry. "Longitudinal Study of Large Language Models During the 2024 US Elections" Submitted to the ACM Conference on Economics and Computation (EC), 2025.
2. A. K. Hopkins*, S. H. Cen*, A. Ilyas, I. Struckman, L. Videgaray, and A. Mądry. "AI Supply Chains: The Complex Ecosystem of AI Actors" Submitted to the International Conference on Machine Learning (ICML), 2025.
3. S. H. Cen* and R. Alur*. "From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing ." ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO). 2024.
4. A. K. Hopkins*, S. H. Cen*, A. Ilyas, I. Struckman, L. Videgaray, and A. Mądry. "AI Supply Chains and Their Implications." Please email for draft. 2024.
5. A. Agarwal, S. H. Cen*, D. Shah, and C. L. Yu. "Network Synthetic Interventions: A Causal Framework for Panel Data with Network Interference." Submitted to Operations Research, 2023.
6. S. H. Cen*, A. Ilyas*, J. Allen, H. Li, D. G. Rand, and A. Mądry. "Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content." ACM Conference on Economics and Computation (EC), 2024. In preparation for submission to Management Science.
7. S. H. Cen*, A. Ilyas*, and A. Mądry. "User Strategization and Trustworthy Algorithms." ACM Conference on Economics and Computation (EC), 2024. Submitted to Operations Research.
8. S. H. Cen*, A. Mądry, and D. Shah. "A User-Driven Framework for Regulating and Auditing Social Media." Pre-print, 2023.
9. C. Zhang*, S. H. Cen*, and D. Shah. "Matrix Estimation for Individual Fairness." International Conference on Machine Learning (ICML), 2023.
10. S. H. Cen*, A. Ilyas*, and A. Mądry. "A Game-Theoretic Perspective on Trust in Recommendation." Responsible Decision Making in Dynamic Environments Workshop at International Conference on Machine Learning (ICML), 2022, Oral.
11. S. H. Cen* and M. Raghavan. "The Right to be an Exception to a Data-Driven Rule." Pre-print, 2022.
12. J. Perolat*, B. De Vylder*, D. Hennes*, [...], S. H. Cen, et al. "Mastering the game of Stratego with model-free multiagent reinforcement learning." Science, 2022.
13. S. H. Cen* and D. Shah. "Regret, stability, and fairness in matching markets with bandit learners." International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
14. S. H. Cen* and D. Shah. "Regulating algorithmic filtering on social media." Conference on Neural Information Processing Systems (NeurIPS), 2021, Spotlight.
15. S. H. Cen* and P. Newman. "Radar-only ego-motion estimation in difficult settings via graph matching." IEEE International Conference on Robotics and Automation (ICRA), 2019.
16. R. Weston*, S. H. Cen, P. Newman, and I. Posner. "Probably Unknown: Deep Inverse Sensor Modelling in Radar." IEEE International Conference on Robotics and Automation (ICRA), 2019.
17. S. H. Cen* and P. Newman. "Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions." IEEE International Conference on Robotics and Automation (ICRA), 2018.
18. S. H. Cen*, V. Srivastava, and N. E. Leonard. "On robustness and leadership in Markov switching consensus networks." IEEE Conference on Decision and Control (CDC), 2017.

Blogs & Media

Blogs

• 3. Blog Series on AI Accountability & Transparency (forthcoming)

Authors: Sarah H. Cen, Cosimo L. Fabrizio, Aleksander Mądry, Martha Minow, and James Siderius.

1. Making Sense of AI Risk Scores
2. Auditing AI: How Much Access Is Needed to Audit an AI System?
3. Ensuring An Adequate Mechanism of Relief for AI-Induced Harms
4. A Public Health Approach to AI Regulation
• 2. Blog Series on AI Deployment and AI Supply Chains

Authors: Sarah H. Cen, Aspen Hopkins, Andrew Ilyas, Aleksander Mądry, Isabella Struckman, and Luis Videgaray.

Read the full report here.

1. We need to talk about AI deployment
2. AI supply chains and why they matter
3. Who will provide AI to the world?
4. Downstream AI Products Benefit (and Suffer) From Access to Upstream AI
• 1. Blog Series on Social Media Regulation

Authors: Sarah H. Cen, Andrew Ilyas, and Aleksander Mądry.

1. Social Media Platforms & the Information Exchange They Run
2. What’s To Know About Regulating Social Media in the US?
3. Why Can't We Regulate Social Media Like Previous Media?
4. Regulating Social Media: Lessons from Existing Regulation

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Media and Videos

• August 2022: Lance Eliot wrote a column in Forbes titled "AI Ethics Fighting Passionately For Your Right To Be An Exception" based on my paper "The Right to be an Exception to a Data-Driven Rule" with Manish Raghavan!
• July 2022: I was honored to give a talk at the Simons Institute for the Theory of Computing on "The Design and Governance of Human-Facing Algorithms" at the workshop for AI and Humanity!
• May 2022: I was lucky enough to have MIT News feature my research in the article "Living better with algorithms" by Grace Chua!
• May 2022: I am grateful to have been selected as a Thriving Star of AI at MIT!
• December 2021: My paper on "Regulating algorithmic filtering on social media" written with Devavrat Shah was featured as a Spotlight at Neurips 2021!
• May 2019: My paper on "Radar-only ego-motion estimation in difficult settings via graph matching" written with Paul Newman was presented at ICRA 2019!
• May 2018: My paper on "Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions" written with Paul Newman was presented at ICRA 2018!

Experience

Honors

More information

Teaching

"Introduction to principles of Bayesian and non-Bayesian statistical inference. Hypothesis testing and parameter estimation, sufficient statistics; exponential families. EM agorithm. Log-loss inference criterion, entropy and model capacity. Kullback-Leibler distance and information geometry. Asymptotic analysis and large deviations theory. Model order estimation; nonparametric statistics. Computational issues and approximation techniques; Monte Carlo methods. Selected topics such as universal inference and learning, and universal features and neural networks."

"The Science Policy Bootcamp is a 5-day short course, offered during MIT's Independent Activities Period in January, designed to introduce participants to the 'nuts and bolts' of science policy making. The course provides an opportunity for young scientists and engineers interested in science policy issues to increase their understanding about and practical involvement with science policy. The bootcamp serves to both expose participants to the fundamental structure and dynamics of science policy and inform them of routes into a policy experience or career."

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Interests and a Note

I love playing tennis in my spare time. I once played on the Varsity (or 1sts) team at Oxford, since then playing more casually with club teams and friends. I also enjoy writing, with my current outlet being through research though, once upon a time, I enjoyed writing short fiction. Creating a welcoming and thoughtful environment is important to me - that means different things to different people, and I'm always open to hearing different opinions. My main goal is to make sure the people around me are heard and thrive.

Programming Languages

• C  |
• C++  |
• Python  |
• MATLAB  |
• Java  |
• HTML  |
• JS