Sarah Cen

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

I am a Ph.D. student in EECS at MIT, advised by Prof. Devavrat Shah in the Laboratory for Information and Decision Systems. My work uses tools from statistical learning theory and economics to study the relationship between performance and social responsibility in machine learning. I am interested in both the development and governance of data-driven algorithms, and my research lies at the intersection of machine learning theory and AI ethics.

Recently, I have written on how to audit social media algorithms, 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.


Research

To see my research experiences, click here.

Broad research interests: statistical learning, causal inference, learning & games, governance of data-driven algorithms, AI ethics.


Publications

  1.  S. H. Cen and Manish Raghavan. "The Right to be an Exception in Data-Driven Decision-Making." Under review. 2022.
  2.  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.
  3.  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.
  4.  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.
  5.  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.
  6.  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.
  7.  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.

Presentations

  • S. H. Cen and D. Shah. "Regret, stability, and fairness in matching markets with bandit learners," 2021. Posters at NeurIPS Strat'ML Workshop and WiDS; Talk at LIDS Stats Talk.
  • S. H. Cen and D. Shah. "Regulating algorithmic filtering on social media," 2021. Talks at NeurIPS (Spotlight) and LIDS Student Conference; Poster at ICWSM 2021 Information Credibilities Workshop.
  • S. H. Cen and D. Shah. "Regulating algorithmic filtering on social media," 2020. Talks at MIT ML Tea Talks, Stanford GSB Rising Scholars Conference, MIT AI Ethics Group, WiDS Cambridge, MLxMIT.
  • S. H. Cen and P. Newman "Radar-only ego-motion estimation in difficult settings via graph matching," 2019. Poster at IEEE Intl. Conf. on Robotics and Automation.
  • S. H. Cen and P. Newman "Precise Ego-Motion Estimation with Millimeter-Wave Radar under Diverse and Challenging Conditions," 2018. Poster at IEEE Intl. Conf. on Robotics and Automation (ICRA). Talk at Oxford Women in Engineering Symposium.
  • S. H. Cen, V. Srivastava, and N. E. Leonard. "On robustness and leadership in Markov switching consensus networks," 2017. Talk at IEEE Conference on Decision and Control (CDC).
  • S. H. Cen and V. Kumar. "Autonomous Flight and Landing of Quadrotor on a Moving Ground Vehicle using Vision-Based Control," 2014. Talk at UPenn GRASP Laboratory. Poster at Grace Hopper Celebration.

Education

Massachusetts Institute of Technology

Ph.D. in Electrical Engineering and Computer Science

Advised by Devavrat Shah

Expected 2024

University of Oxford

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

Advised by Paul Newman

2018

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

2016

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

Experience

Researcher (Ph.D.) in MIT EECS

Laboratory for Information & Decision Systems

Advised by Prof. Devavrat Shah. Developing algorithms to accurately predict potential outcomes (e.g., the effect of mask mandates or body-cameras on hospitalization rates or police use of force) under limited and/or imperfect data. 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 statistical learning, 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.

2015

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.

2014

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.

2013

Honors

MIT EECS Thriving Star | Speaker at “The Thriving Stars of AI” Research Summit
2022
Hugh Hampton Young Fellowship | MIT award for academic achievement & character
2020
Chyn Duog Shiah Fellowship | Awarded to one MIT engineering student
2020
Ida M. Green Fellowship | Awarded to eight MIT graduate women
2018
Edwin S. Webster Fellowship | MIT EECS award
2018
Sachs Oxford Scholarship | Funds one Princeton student to study at Univ. of Oxford
2016
Top Thesis | Princeton University School of Engineering & Applied Sciences
2016
Top Thesis | Princeton University Department of Mechanical Engineering
2016
Top Academic Performance | Princeton University Department of Mechanical Engineering
2016
Graduate with Highest Honors | Princeton University
2016
Phi Beta Kappa, Sigma Xi, and Tau Beta Pi | Honor Societies
2016
Palantir Scholarship for Women in Engineering | Finalist
2015
Best Presentation Award | University of Pennsylvania GRASP REU
2014
Grace Hopper Celebration Poster Scholarhsip
2014
Top 3 Percent of Class | Princeton University Shapiro Prize for Academic Excellence
2014

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
2019
MIT Global Startup Workshop | Team Lead for conference on entrepreneurship
2018 - 2019
Oxford Females in Engineering, Science & Technology | VP, Conference Team
2018
Associate Editor and Special Session Leader | Intl. Transportation Systems Conference
2018
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

Teaching

Science & Technology Policy Bootcamp | Teaching assistant | Bill Bonvillian
2021

"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
2020

"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