Sarah H. Cen

Massachusetts Institute of Technology · Cambridge, MA · shcen at mit dot edu

I am a Ph.D. student (MIT EECS) advised by Prof. Devavrat Shah and Prof. Aleksander Mądry. My research lies at the intersection of machine learning theory and AI ethics. I am interested in the design of data-driven algorithms as well as their governance, and my work uses tools from statistics, causal inference, and economics.

Recently, I have written on how to audit social media algorithms, the estimation of counterfactual potential outcomes under spillover effects, how competing for resources under uncertainty affects long-term outcomes, and the individual-level rights of data-driven decision subjects. 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 Unversity of Oxford. As an undergraduate, I studied control & decision systems with Prof. Naomi Leonard at Princeton University.


To see my research experiences, click here.

Publications and Pre-prints

*First-author contribution.

  1. A. Agarwal, S. H. Cen*, D. Shah, and C. L. Yu. "Network Synthetic Interventions: A Causal Framework for Panel Data with Network Interference." Pre-print. 2022.
  2. S. H. Cen* and M. Raghavan. "The Right to be an Exception to a Data-Driven Rule." Pre-print. 2022.
  3. J. Perolat*, B. De Vylder*, D. Hennes*, [...], S. H. Cen, et al. "Mastering the game of Stratego with model-free multiagent reinforcement learning." In Science. 2022.
  4. S. H. Cen* and D. Shah. "Regret, stability, and fairness in matching markets with bandit learners." In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  5. S. H. Cen* and D. Shah. "Regulating algorithmic filtering on social media." Spotlight paper. In Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021.
  6. S. H. Cen* and P. Newman. "Radar-only ego-motion estimation in difficult settings via graph matching." In Proceedings of the IEEE Intl. Conf. on Robotics and Automation (ICRA), 2019.
  7. R. Weston*, S. H. Cen, P. Newman, and I. Posner. "Probably Unknown: Deep Inverse Sensor Modelling in Radar." In Proceedings of the IEEE Intl. Conf. on Robotics and Automation (ICRA), 2019.
  8. S. H. Cen* and P. Newman. "Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions." In Proceedings of the IEEE Intl. Conf. on Robotics and Automation (ICRA), 2018.
  9. S. H. Cen*, V. Srivastava, and N. E. Leonard. "On robustness and leadership in Markov switching consensus networks." In Proceedings of the IEEE Conference on Decision and Control (CDC), 2017.

Selected Recent Talks

Media and Videos


Massachusetts Institute of Technology

Ph.D. in Electrical Engineering and Computer Science

Advised by Devavrat Shah and Prof. Aleksander Mądry

Expected 2024

University of Oxford

M.Sc. by Research in Engineering Science
Thesis: "Radar-only ego-motion estimation and localization"

Advised by Paul Newman


Princeton University

B.S.E. in Mechanical Engineering
Certificates in (1) Computer Science and (2) Robotic & Intelligent Systems
Thesis: "Optimal leader selection for dynamic networks modeled as Markov jump linear systems"

Advised by Naomi Leonard


Selected Coursework
  • 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


Researcher (Ph.D.) in MIT EECS

Lab for Information & Decision Systems + Computer Science & Artificial Intelligence Lab

Advised by Prof. Devavrat Shah and Prof. Aleksander Mądry. Designing algorithms to predict the potential outcomes (e.g., the effect of mask mandates or body-cameras on hospitalization rates or police use of force) under spillover effects. Concurrently, studying how data-driven decisions impact humans in various contexts, such as how social media affects users and how matching algorithms affect long-term fairness. Our work uses tools from statistics, causal inference, economics, law, and sociology.

2018 - Present

Researcher (M.Sc.) at the University of Oxford

Oxford Robotics Institute

Advised by Prof. Paul Newman. Developed state-of-the-art algorithms for radar-only odometry using graph matching. Successfully tested on autonomous vehicles under adverse and diverse conditions. Designed neural network that operates under weak supervision and noisy data.

2016 - 2018

Researcher (B.S.E.) at Princeton University

Dynamical Control Systems Lab

Advised by Prof. Naomi Leonard. Derived novel metrics quantifying the robustness of consensus and the reference tracking accuracy of dynamic leader-follower networks modeled as Markov switching graphs. With Ph.D. student, bounded regret of optimal fully-informed multi-armed bandit.

2014 - 2016

Research Intern at MIT Lincoln Laboratory

Space Systems and Technology Division

Supervised by Dr. Yaron Rachlin. Greatly improved the recovery of satellite images taken with limited sensing capabilities and under adverse conditions (e.g., noise and jitter) using a particle filter. Applied tools from signal processing, computer vision, and statistical inference.


Research Intern at UPenn GRASP Lab

Multi-Robot Systems Lab

Advised by Prof. Vijay Kumar. Implemented the autonomous tracking and landing of a quadrotor (equipped with only a monocular camera and IMU) on a moving ground vehicle. Programmed and tested flight controller, motion tracker, and extended Kalman filter.


Software Development Intern at Wattvision

Real-time energy monitoring systems

Supervised by Savraj Singh. Designed and implemented new user interfaces (UIs) for both the mobile and web sites. Repaired bugs, maintained website backend, and integrated external hardware.



MIT EECS Thriving Star | Speaker at “The Thriving Stars of AI” Research Summit
Hugh Hampton Young Fellowship | MIT award for academic achievement & character
Chyn Duog Shiah Fellowship | Awarded to one MIT engineering student
Ida M. Green Fellowship | Awarded to eight MIT graduate women
Edwin S. Webster Fellowship | MIT EECS award
Sachs Oxford Scholarship | Funds one Princeton student to study at Univ. of Oxford
Top Thesis | Princeton University School of Engineering & Applied Sciences
Top Thesis | Princeton University Department of Mechanical Engineering
Top Academic Performance | Princeton University Department of Mechanical Engineering
Graduate with Highest Honors | Princeton University
Phi Beta Kappa, Sigma Xi, and Tau Beta Pi | Honor Societies
Palantir Scholarship for Women in Engineering | Finalist
Best Presentation Award | University of Pennsylvania GRASP REU
Grace Hopper Celebration Poster Scholarhsip
Top 3 Percent of Class | Princeton University Shapiro Prize for Academic Excellence

More information

Leadership positions

MIT AI Ethics Reading Group | Co-organizer for institute-wide group
2020 - 2021
Science Policy Initiative | Bootcamp Director & Executive Board member
2020 - 2021
IDSS Student Council | Diversity, Equity & Inclusion Officer
2020 - 2021
Graduate Women in MIT EECS | Co-President
MIT Global Startup Workshop | Team Lead for conference on entrepreneurship
2018 - 2019
Oxford Females in Engineering, Science & Technology | VP, Conference Team
Associate Editor and Special Session Leader | Intl. Transportation Systems Conference
Oxford University Women's Tennis | Varsity Player
2016 - 2018
Princeton Robotics Club | Project Leader
2013 - 2016
Princeton Club Tennis | Captain and President
2012 - 2016
The Daily Princetonian | Web Editor and News Reporter
2012 - 2014


Science & Technology Policy Bootcamp | Teaching assistant | Bill Bonvillian

"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."

Inference and Information | Teaching assistant | Gregory Wornell and Lizhong Zheng

"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."

Programming Languages

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