Esther Rolf

My research interests lie in the union of machine learning, statistics, and algorithms. Currently I am studying how data acquisition influences the efficacy and applicability of machine learning systems, and I'm particularly interested in problems with the potential for positive social impact. My projects span developing algorithms and infrastructure for reliable environmental monitoring using machine learning and understanding social outcomes of decisions influenced by machine learning systems.

I'm a member of the Berkeley AI Research (BAIR) Lab, and a fellow in the Global Policy Lab. During my Ph.D. I have graciously received support from an NSF GRFP grant and a Google Ph.D. fellowship.

In my free time I enjoy trail running, cooking new foods, and volunteering as a manicurist at a local assisted living center.


April 8, 2021: I'll be speaking as a Harvard CRCS Rising Star in AI for Social Good. You can register for the session or find the recording using the underlined link!
May - July 2021: I'll be interning at Microsoft Research, advised by Nebojsa Jojic!
July, 2021: Our paper on the importance of subgroup representation in training data was accepted to ICML 2021! Find our poster at the virtual conference on Wednesday, July 21, 9-11pm PDT!
July, 2021: Our paper on MOSAIKS: "A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery" is published at Nature Communications!



  • Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
    Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan. ICML, 2021.
    [paper] [github]
  • A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
    Esther Rolf*, Jonathan Proctor*, Tamma Carleton*, Ian Bolliger*, Vaishaal Shankar*, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang. Nature Communications, 2021.
    [paper] [project page] [social impact statement] [github]
  • Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
    Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock. ICML, 2020.
    Workshop paper presented at NeurIPS Joint Workshop on AI for Social Good, 2019 Best Paper Award .
    [full paper] [workshop paper] [github]
  • The effect of large-scale anti-contagion policies on the COVID-19 pandemic
    Solomon Hsiang, Daniel Allen*, Sébastien Annan-Phan*, Kendon Bell*, Ian Bolliger*, Trinetta Chong*, Hannah Druckenmiller*, Luna Yue Huang*, Andrew Hultgren*, Emma Krasovich*, Peiley Lau*, Jaecheol Lee*, Esther Rolf*, Jeanette Tseng*, Tiffany Wu*. Nature, 2020.
    [paper] [github]
  • A Successive-Elimination Approach to Adaptive Robotic Source Seeking
    Esther Rolf*, David Fridovich-Keil*, Max Simchowitz, Benjamin Recht, Claire Tomlin. IEEE Transactions on Robotics, 2020.
    [paper] [video]
  • Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
    Esther Rolf, Michael I. Jordan, Benjamin Recht. AISTATS, 2020.
    [paper] [github]
  • Generalized Planetary Remote Sensing
    Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang. Presented at AGU Fall meeting, 2019.
  • Delayed Impact of Fair Machine Learning
    Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt. ICML, 2018 Best Paper Award.
    [paper] [BAIR blog] [github]
  • Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis
    Ian Bolliger, Tamma Carleton, Solomon Hsiang, Jonathan Kadish, Jonathan Proctor, Benjamin Recht, Esther Rolf, Vaishaal Shankar. Presented at Bloomberg Data for Good Exchange, 2017.

Curriculum Vitae

You can access my CV here.


You can contact me by email at firstname underscore lastname at berkeley dot edu.