Esther's research blends methodological and applied techniques to study and design machine learning algorithms and systems with an emphasis on usability, data-efficiency and fairness. Some of her specific projects span developing听algorithms and infrastructure for reliable environmental monitoring using machine learning, responsible and fair algorithm design and use, and the influence of data acquisition and representation on the efficacy and applicability of machine learning systems.
Esther is currently a fellow funded by the Harvard Data听Science Initiative and the Center for Research on Computation and Society.听She completed her PhD in听Computer听Science听at UC Berkeley in 2022, where she was advised by Ben Recht and Michael I. Jordan. Esther鈥檚 PhD was supported by an NSF Graduate Research Fellowship, a Google Research Fellowship, and a UC Berkeley Stonebreaker Fellowship. Esther has won best paper awards at ICML (2018) and the Workshop on AI for Social Good at NeurIPS (2019).
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