PeopleofSuperintelligence

Researcher

Cade Gordon

Cade Gordon is a machine-learning researcher whose work spans video generation, image-text learning and AI for biological design. He coauthored LAION-5B, an open dataset of 5.85 billion image-text pairs that earned a 2022 NeurIPS Outstanding Paper Award, and contributed to research on contrastive-model scaling. His later research applies language models to therapeutic-antibody design and protein-fitness prediction, with work at Cohere, BigHat Biosciences and FutureHouse. He is a computer science doctoral student at Stanford University after studying and researching at UC Berkeley.

Timeline

  1. 2020

    Video generation with neural differential equations

    In 2020, he and Natalie Parde coauthor “Latent Neural Differential Equations for Video Generation.” Their study uses neural differential equations to model video dynamics and reports an Inception Score of 15.20 on unconditional 64×64 video generation.

  2. 2022

    LAION-5B earns a NeurIPS award

    He coauthors LAION-5B, an open dataset of 5.85 billion CLIP-filtered image-text pairs. The 2022 NeurIPS paper receives an Outstanding Paper Award.

  3. 2023

    Contrastive image-text scaling

    In 2023, he coauthors “Reproducible scaling laws for contrastive language-image learning.” The study uses public LAION data and OpenCLIP to examine performance across models trained on up to two billion image-text pairs, and releases its evaluation workflow and models.

  4. September 2024

    A Siebel scholarship

    Berkeley names him to the Siebel Scholars Foundation’s class of 2025 for his research on protein language models for biological design.

  5. December 2024

    Generative antibody humanization

    In 2024, he and three coauthors reframe therapeutic-antibody humanization as conditional generation, sampling mutations from a language model trained on human antibody data. The method aims to reduce immunogenicity while retaining or improving therapeutic properties.

  6. By 2025

    AI for drug discovery

    By 2025, his research career includes work at Cohere, BigHat Biosciences and FutureHouse. At BigHat, he develops methods for safer antibody therapeutics; at FutureHouse, he creates autonomous protein-engineering systems.

  7. 2025

    Protein language model fitness

    His 2025 Berkeley EECS master’s thesis with Amy Lu and Pieter Abbeel studies whether protein language model sequence likelihood predicts protein fitness. The work appears at ICLR 2025, and he is a 2025 Hertz Fellow.

  8. Since 2026

    Doctoral work at Stanford

    He begins doctoral research in computer science at Stanford University in 2026.

Career

  1. University of California, BerkeleyResearcher

Education

  1. From 2026Stanford UniversityDoctoral student
  2. University of California, BerkeleyMaster's

From the biography.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of California, Berkeley. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of California, Berkeley. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of California, Berkeley. 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.
  4. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes University of California, Berkeley. 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.

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. [2011.03864] Latent Neural Differential Equations for Video Generation arxiv.org. Checked 20 October 2026.
  2. NeurIPS 2022 Awards neurips.cc. Checked 20 October 2026.
  3. LAION-5B: An open large-scale dataset for training next generation image-text models proceedings.neurips.cc. Checked 20 October 2026.
  4. Cade Gordon cadegordon.io. Checked 20 October 2026.
  5. [2212.07143] Reproducible scaling laws for contrastive language-image learning arxiv.org. Checked 20 October 2026.
  6. Six students honored as Siebel Scholars - Berkeley Engineering engineering.berkeley.edu. Checked 20 October 2026.
  7. [2412.04737] Generative Humanization for Therapeutic Antibodies arxiv.org. Checked 20 October 2026.
  8. Cade Gordon - Hertz Foundation hertzfoundation.org. Checked 20 October 2026.
  9. Protein Language Model Fitness is a Matter of Preference | EECS at UC Berkeley www2.eecs.berkeley.edu. Checked 20 October 2026.
  10. Hertz Fellows Directory - Hertz Foundation hertzfoundation.org. Checked 20 October 2026.