I am a Senior Machine Learning Scientist at Genentech, where I am part of the Frontier Research team within Prescient Design. My research works toward trustworthy scientific inquiry and decision-making using machine learning, with a current focus on uncertainty quantification and statistical inference methods.

I received my Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in August 2023, where I was advised by Michael I. Jordan and Jennifer Listgarten as an NSF Graduate Research Fellow. Prior to Berkeley, I was a Google AI Resident and also conducted marine biology fieldwork with Kakani Katija at the Monterey Bay Aquarium Research Insitute. I received my B.S. in Computer Science in 2016 from Stanford University.

Beyond research, I am an avid hiker, religious fan of artistic gymnastics and aroids, and proud mom to an extraordinary son.

My email is clarafy at berkeley dot edu. Please reach out if you would like to chat!

select recent work

(\(\alpha\)-\(\beta\)) denotes alphabetical ordering.

• (\(\alpha\)-\(\beta\)) Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, and Tijana Zrnic. 2023. Prediction-powered inference. Science, 382, 669-674. publication arXiv code talk

• Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, and Michael I. Jordan. 2022. Conformal prediction under feedback covariate shift for biomolecular design. Proceedings of the National Academy of Sciences, 119(43), e2204569119. arXiv publication PDF code bibtex talk
  

talks

Prediction-powered inference

• Simons Institute Workshop on AI-Science: Strengthening the Bond Between the Sciences and AI, June 14, 2024.
• Machine Learning for Health Care and Life Sciences Workshop, ETH AI Center, November 30, 2023.
• Alaa Lab, UCSF/UC Berkeley Computational Precision Health, September 29, 2023. recording
• Microbiology Society Annual Conference, April 17, 2023.

Conformal prediction for biomolecular design

• EPFL Physics of Living Systems Seminar Series, November 18, 2022.
• Machine Learning for Protein Engineering Seminar Series, October 18, 2022. recording
• AIDD Summer School on Advanced Machine Learning for Drug Disovery, May 13, 2022.

Autofocused oracles for model-based design

• Debora Marks Lab, Harvard Medical School, August 2020.

Dissertation

• Trustworthy scientific inquiry and design with machine learning. UC Berkeley EECS Ph.D. Dissertation Talk, May 10, 2023. recording (technical portion only)

This website was built using Michael Rose’s So Simple Jekyll theme.

"A man can't be always defending the truth; there must be a time to feed on it."
C. S. Lewis, Reflections on the Psalms (1958)