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 interested in the interplay between optimization and learning, and I have worked specifically on topics in optimal control, machine learning, and computational imaging. 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.

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).

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.

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),
early version awarded Best Student Paper in Imaging Systems at OSA Congress 2018.

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.

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

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

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.

Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information
Sarah Dean, Sarah Rich, and Benjamin Recht
to appear at FAT* 2020.

*I have made my application available for reference here.

a woman among giants
Last updated 16 December 2019.