PeopleofSuperintelligence

Researcher

Chloe Hsu

Also known as Chloe Ching-Yun Hsu

Chloe Hsu is a machine learning researcher and a Founding Team Member at Safe Superintelligence Inc. whose work covers the design and engineering of proteins. Before graduate school, she studied mathematics and theoretical computer science at Caltech, worked as a machine learning engineer at Google Health, and coauthored a 2017 arXiv paper on fast generalized discrete Fourier transforms with Chris Umans. At UC Berkeley, advised by Jennifer Listgarten and Moritz Hardt, she became a Microsoft Research PhD Fellow in 2021 and published a 2022 Nature Biotechnology paper on protein fitness models. In 2022, she and colleagues including Alexander Rives trained ESM-IF1 on structures that AlphaFold2 predicted for 12M protein sequences, and the ICML paper describing it won an Outstanding Paper Runner Up award. After publishing a 2024 Nature Biotechnology primer on generative protein models and completing her Berkeley dissertation in December 2024, she studies new machine learning approaches at Safe Superintelligence Inc.

Badges

Timeline

  1. 2017

    Caltech and a Fourier algorithm

    Before graduate school, she studies mathematics and theoretical computer science at Caltech and works as a machine learning engineer at Google Health. In 2017, she and Chris Umans post "A new algorithm for fast generalized DFTs" on arXiv, which gives an algorithm for the generalized discrete Fourier transform over finite groups of Lie type.

  2. 2021

    A Microsoft Research PhD Fellowship

    In 2021, Microsoft Research names her among its Global PhD Fellowship recipients, while she is a PhD student at UC Berkeley advised by Jennifer Listgarten and Moritz Hardt.

  3. 2022

    Protein fitness models in Nature Biotechnology

    In 2022, she publishes "Learning protein fitness models from evolutionary and assay-labeled data" in Nature Biotechnology with Hunter Nisonoff, Clara Fannjiang and Jennifer Listgarten at UC Berkeley. The method combines ridge regression on site-specific amino acid features with one probability density feature from modeling the evolutionary data.

  4. 2022

    The ESM-IF1 inverse folding model

    In 2022, she and colleagues including Alexander Rives, Brian Hie and Adam Lerer present "Learning inverse folding from millions of predicted structures" at ICML. They add training data by predicting structures for 12M protein sequences with AlphaFold2, and the resulting model, released as ESM-IF1, reaches 51% native sequence recovery on structurally held-out backbones.

  5. 2022

    ICML Outstanding Paper Runner Up

    In 2022, she and her coauthors receive an Outstanding Paper Runner Up award at ICML for the inverse folding paper.

  6. 2024

    A primer on generative protein models

    In 2024, she publishes the Nature Biotechnology primer "Generative models for protein structures and sequences" with Clara Fannjiang and Jennifer Listgarten. It asks how generative models, like those behind ChatGPT and DALL-E2, can be useful for protein engineering.

  7. December 2024

    A Berkeley dissertation on proteins

    Her dissertation, "Learning to Design and Engineer Proteins from Evolution, 3D Structures, and Experiments", appears as UC Berkeley EECS Technical Report UCB/EECS-2024-197 in December 2024. She completes her PhD at Berkeley, advised by Jennifer Listgarten and Moritz Hardt.

  8. February 2026

    Safe Superintelligence founding team member

    She works as a Founding Team Member at Safe Superintelligence Inc., where she studies new machine learning approaches. In February 2026, she gives a Sarafan ChEM-H special seminar at Stanford on learning and designing molecular interactions.

Career

  1. Since 2026Safe SuperintelligenceFounding Team Member
  2. GoogleMachine Learning Engineer

Education

  1. 2024University of California, BerkeleyPhD
  2. California Institute of Technology

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Chloe Hsu. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Chloe Hsu. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Safe Superintelligence. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Safe Superintelligence. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Chloe Hsu | 8VC Fellow | 8VC 8vc.com. Checked 22 October 2026.
  2. [1707.00349] A new algorithm for fast generalized DFTs arxiv.org. Checked 22 October 2026.
  3. Microsoft Research 2021 Global PhD Fellowship Recipients - Microsoft Research microsoft.com. Checked 22 October 2026.
  4. Learning protein fitness models from evolutionary and assay-labeled data | Nature Biotechnology nature.com. Checked 22 October 2026.
  5. Learning inverse folding from millions of predicted structures proceedings.mlr.press. Checked 22 October 2026.
  6. GitHub - facebookresearch/esm: Evolutionary Scale Modeling (esm): Pretrained language models for proteins github.com. Checked 22 October 2026.
  7. ICML 2022 Awards icml.cc. Checked 22 October 2026.
  8. Chloe Hsu chloe-hsu.com. Checked 22 October 2026.
  9. Generative models for protein structures and sequences | Nature Biotechnology nature.com. Checked 22 October 2026.
  10. Learning to Design and Engineer Proteins from Evolution, 3D Structures, and Experiments | EECS at UC Berkeley www2.eecs.berkeley.edu. Checked 22 October 2026.
  11. Sarafan ChEM-H Special Seminar: Learning and Designing Molecular Interactions chemh.stanford.edu. Checked 22 October 2026.