About Me  •   News  •   Teaching  •   Advising  •   Publications

Sarah Dean

sdean AT cornell DOT edu

I am an Assistant Professor in the CS Department at Cornell. ⓘ I can't guarantee a reply to every email. I do not have the capacity to supervise remote internships.

I study the interplay between optimization, machine learning, and dynamics in real-world systems. My research focuses on understanding the fundamentals of data-driven methods for control and decision-making, inspired by applications ranging from robotics to recommendation systems. You can learn more by reading my dissertation, watching my dissertation talk, or browsing my recent talks.

In fall 2021, I was a postdoc with Jamie Morgenstern at UW. Before that, I was a PhD student in EECS at UC Berkeley, advised by Ben Recht. At Berkeley, I was a founding member of Graduates for Engaged and Extended Scholarship in computing and Engineering (GEESE). I interned with Canopy in Boston, MA during Summer 2019. ⓘ For reference, my job talk, CV, faculty app, and NSF GRFP app.

News

• [new!] Initializing Services in Interactive ML Systems for Diverse Users was accepted to NeuRIPS 2024 in Vancouver
• [new!] I spoke at the Lehigh AIR Lab Seminar on the Foundations for Learning with Human Interaction & Dynamics
• My collaborators presented Random Features Approximation for Control-Affine Systems at L4DC 2024, Learning from Streaming Data when Users Choose at ICML 2024, and Harm Mitigation in Recommender Systems under User Preference Dynamics at KDD 2024
• During summer 2024, I spoke at the Princton OLC Workshop on Learning Dynamics from Bilinear Observations; gave a plenary at SYSID 2024 on Learning Models of Dynamical Systems from Finite Observations; and spoke at the ICML FoRLaC workshop and Workshop on Humans, ADS, and Society
• My coauthors presented Strategic Usage in a Multi-Learner Setting and Emergent specialization from participation dynamics and multi-learner retraining at AISTATS 2024 and Ranking with Long-Term Constraints at WSDM 2024
• I co-organized NESCW 2024 and RecEcoSys at AAAI 2024
• In 2023, we had papers at NeuRIPS, a CoRL workshop, AIES, IFAC, and ICLR
• I co-organized the DM4IR&Recsys workshop at WWW 2023 and spoke on User Dynamics in Machine Learning Systems at the Princeton Networks & Cognition Workshop and OCO with Unbounded Memory at ACC Workshop
• I gave a keynote at L4DC 2022 on Preference and Participation Dynamics in Learning Systems
• I joined Cornell CS in 2022
• In 2021, I was on the job market, graduated from Berkeley, and did a post doc at UW

Teaching

At Cornell, I am teaching CS 3/5780: Introduction to Machine Learning in Fall 2024. Previously, I have taught CS 4/5789: Introduction to Reinforcement Learning (in Spring 2022, 2023, 2024) and Machine Learning in Feedback Systems (CS 6784) in Fall 2022, 2023).

At Berkeley, I worked on course development for a new EECS Anti-Racism and Social Justice course (and gave a guest lecture on Bias in Algorithms) and as a Graduate Student Instructor for Statistical Learning Theory (EECS 281) and Introduction to Machine Learning (EECS 189/289).

Advising and Mentoring

I've had the pleasure of working with several students at Cornell, including PhD advisees Kimia Kazemian, Rohan Banerjee (co-advised with Tapo Bhattacharjee), and Raunak Kumar ('24, co-advised with Bobby Kleinberg); MS advisees Eliot Shektman ('24) and Vaishnavi Gupta; undergraduates Bradley Guo (2024-present), Janna Lin (2024-present), Myles Pasetsky (2024-present), Yijia Dai (2023-2024) Vadim Popov (2023), Rachael Close (2023), Amelia Kovacs (2022-2024), Alexis Hao (2022), Emily Mei (2022), Fengyu Li (2022), Jiwoo Cheon (2022).

Publications

ⓘ My Google Scholar profile has the most up to date list of publications and preprints. If you are interested in code for a paper without a github link, feel free to send me an email.

Learning Linear Dynamics from Bilinear Observations [arXiv] [slides] [talk]
Yahya Sattar, Yassir Jedra, Sarah Dean.
in submission.

Accounting for AI and Users Shaping One Another: The Role of Mathematical Models [arXiv]
Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi.
shorter version Recommender Systems as Dynamical Systems: Interactions with Viewers and Creators presented at the AAAI 2024 Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design.

Initializing Services in Interactive ML Systems for Diverse Users [arXiv]
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern, Sarah Dean, Lillian J.Ratliff, Maryam Fazel.
to appear at NeuRIPS 2024.

Harm Mitigation in Recommender Systems under User Preference Dynamics [arXiv] [github]
Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis.
presented at KDD 2024.

Learning from Streaming Data when Users Choose [arXiv] [github]
Jinyan Su, Sarah Dean.
presented at ICML 2024.

Random Features Approximation for Control-Affine Systems [arXiv]
Kimia Kazemian, Yahya Sattar, Sarah Dean.
presented at L4DC 2024.
short version presented at NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems 2023.

Strategic Usage in a Multi-Learner Setting [arXiv] [github]
Eliot Seo Shekhtman, Sarah Dean.
presented at AISTATS 2024.
short version presented (oral) at AAAI 2024 Recommendation Ecosystems Workshop: Modeling, Optimization, and Incentive Design.

Emergent specialization from participation dynamics and multi-learner retraining [arXiv] [slides] [talk]
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, and Maryam Fazel
presented at AISTATS 2024.

To Ask or Not To Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding [arXiv] [website]
Rohan Banerjee, Rajat Kumar Jenamani, Sidharth Vasudev, Amal Nanavati, Sarah Dean, Tapomayukh Bhattacharjee
in submission
short version presented at First Workshop on Out-of-Distribution Generalization in Robotics at CoRL 2023.

Ranking with Long-Term Constraints [arXiv] [github]
Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims
presented at WSDM 2024.

Online Convex Optimization with Unbounded Memory [arXiv] [slides]
Raunak Kumar, Sarah Dean, Robert D. Kleinberg
presented at NeuRIPS 2023.

Reward Reports for Reinforcement Learning [arXiv]
Thomas Krendl Gilbert, Sarah Dean, Nathan Lambert, Tom Zick, and Aaron Snoswell
presented at AIES 2023.
short version at Responsible Decision Making in Dynamic Environments workshop at ICML 2022.

Perception-Based Sampled-Data Optimization of Dynamical Systems [arXiv]
Liliaokeawawa Cothren, Gianluca Bianchin, Sarah Dean, Emiliano Dall'Anese
presented at IFAC 2023.

Modeling Content Creator Incentives on Algorithm-Curated Platforms [arXiv] [overview]
Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, and Sarah Dean
oral presentation at ICLR 2023.

Engineering a Safer Recommender System [PDF]
Liu Leqi and Sarah Dean
at Responsible Decision Making in Dynamic Environments workshop at ICML 2022.

Preference Dynamics Under Personalized Recommendations [arXiv] [slides] [talk]
Sarah Dean and Jamie Morgenstern
presented at EC 2022.

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems [CLTC] [arXiv]
Thomas Krendl Gilbert, Sarah Dean, Tom Zick, and Nathan Lambert
Center for Long-Term Cybersecurity Whitepaper Series (2022).

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty [arXiv]
Andrew J. Taylor*, Victor D. Dorobantu*, Sarah Dean*, Benjamin Recht, Yisong Yue, and Aaron D. Ames
presented at CDC 2021.

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability [arXiv] [github]
Mihaela Curmei*, Sarah Dean*, and Benjamin Recht
presented at ICML 2021,
short version presented at Participatory Approaches to Machine Learning workshop at ICML 2020.

Certainty Equivalent Perception-Based Control [arXiv] [github] [talk] [slides]
Sarah Dean and Benjamin Recht
oral presentation at L4DC 2021.

Axes for Sociotechnical Inquiry in AI Research [arXiv] [IEEE]
Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, and Tom Zick
published in IEEE Transactions on Technology and Society (2021).

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks [arXiv]
McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, and Tom Zick
presented at IEEE ISTAS 2020.

Do Offline Metrics Predict Online Performance in Recommender Systems? [arXiv] [github]
Karl Krauth, Sarah Dean*, Alex Zhao*, Wenshuo Guo*, Mihaela Curmei*, Benjamin Recht, and Michael I. Jordan
presented at the Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020.

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions [arXiv] [video] [talk] [slides] [github]
Sarah Dean, Andrew Taylor, Ryan Cosner, Benjamin Recht, and Aaron Ames
Best Paper Finalist at CoRL 2020.

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning [arXiv] [github]
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T Liu, Daniel Björkegren, Moritz Hardt, and Joshua Blumenstock
presented at ICML 2020,
short version awarded Best Paper at NeurIPS Joint Workshop on AI for Social Good 2019.

Robust Guarantees for Perception-Based Control [arXiv] [slides] [talk] [poster] [github]
Sarah Dean, Nikolai Matni, Benjamin Recht, and Vickie Ye
presented at L4DC 2020.

Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information [arXiv] [talk]
Sarah Dean, Sarah Rich, and Benjamin Recht
presented at FAccT 2020.

On the Sample Complexity of the Linear Quadratic Regulator [arXiv] [FoCM] [talk]
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu
published in Foundations of Computational Mathematics (2019).

High-throughput fluorescence microscopy using multi-frame motion deblurring [BOE] [github]
Zachary Phillips, Sarah Dean, Laura Waller, and Benjamin Recht
published in Biomedical Optics Express 11 (2020),
extended abstract awarded Best Student Paper in Imaging Systems at OSA Congress 2018.

Safely Learning to Control the Constrained Linear Quadratic Regulator [arXiv] [slides]
Sarah Dean, Stephen Tu, Nikolai Matni, and Benjamin Recht
presented at ACC 2019.

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator [arXiv] [github]
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu
presented at NeurIPS 2018.

Delayed Impact of Fair Machine Learning [arXiv] [Bloomberg] [BAIR Blog]
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt
Best Paper Award at ICML 2018.

A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics [arXiv]
Roel Dobbe, Sarah Dean, Thomas Gilbert, and Nitin Kohli
presented at FAT/ML 2018.

ⓘ Last updated September 2024.