Sarah Dean


sarahdean AT eecs DOT berkeley DOT edu

Sarah Dean

I am a PhD student in EECS at UC Berkeley, working with Ben Recht and affiliated with BAIR and BCCI. I am funded by a Berkeley fellowship and the NSF graduate research fellowship.* I am broadly interested in designing methods for and understanding the implications of data-driven optimization, and I have worked specifically in optimal control, machine learning, and computational imaging. My work is motivated by applications in robotics, developmental economics, and recommender systems. I am 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.

I graduated from the University of Pennsylvania in 2016, where I studied electrical engineering and math and had the pleasure of working with professors Daniel Lee and Daniel Koditschek. During my time at Penn, I worked as a teaching assistant for several math and engineering courses and became engaged in service learning through my involvement with the West Philadelphia Tutoring Project at Civic House. I grew up in upstate New York, and I like to spend my time pretending to be athletic outside: hiking, biking, swimming, sailing, and cross country skiing.

Research

For the most up to date list of my publications and preprints, see my Google Scholar profile.

Learning and Control

Robust Guarantees for Perception-Based Control [arXiv] [slides] [talk]
Sarah Dean, Nikolai Matni, Benjamin Recht, and Vickie Ye
submitted.

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

Safely Learning to Control the Constrained Linear Quadratic Regulator [arXiv]
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.

Outcome-Aware Machine Learning

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

Balancing Competing Objectives for Welfare-Aware Machine Learning with Imperfect Data [PDF]
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T Liu, Daniel Björkegren, Moritz Hardt, and Joshua Blumenstock
Best Paper Award at NeurIPS Joint Workshop on AI for Social Good 2019.

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.

Computational Imaging

High-throughput fluorescence microscopy using multi-frame motion deblurring [BOE]
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.

*I have made my application available for reference here.

a woman among giants
Last updated 8 March 2020.