Genentech: Clara Wong-Fannjiang | Machine Learning Scientist, Frontier Research, Prescient Design, Computational Sciences

Scientists / Our Scientists

Awards & Honors

I joined Prescient Design at Genentech in September 2023. I received my Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in August 2023, advised by Michael I. Jordan and Jennifer Listgarten. Prior to Berkeley, I was a Google AI resident and researcher at the Monterey Bay Aquarium Research Institute, and received a B.S. in Computer Science from Stanford University.

Featured Publication

Conformal prediction under feedback covariate shift for biomolecular design.

Proceedings of the National Academy of Sciences, 119(43), e2204569119.

Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, and Michael I. Jordan.

View Abstract

How can we use machine-learning systems to accelerate scientific discovery in provably reliable ways? How can we ensure that the outputs of generative models exhibit desired properties, and that we can make trustworthy decisions based on the outputs of predictive models? My research develops uncertainty quantification and statistical inference methods to tackle these questions, particularly in the context of drug discovery.

University of California, Berkeley, Ph.D. in Electrical Engineering and Computer Sciences – 2023
Stanford University, B.S. in Computer Science – 2016

NSF Graduate Research Fellowship – 2019

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Clara Wong-Fannjiang

Machine Learning Scientist, Frontier Research, Prescient Design, Computational Sciences

Featured Publication

Conformal prediction under feedback covariate shift for biomolecular design.