PeopleofSuperintelligence

Researcher

Dan Berenberg

Also known as Daniel Berenberg

Dan Berenberg is a machine learning researcher whose work spans protein function prediction, protein design and self-organizing neural networks. After earning mathematics and computer science degrees at the University of Vermont, he worked on protein modeling at the Flatiron Institute and joined Genentech with Prescient Design in 2021, where he coauthored DeepFRI. He coauthored methods for antibody sequence sampling and protein homology; his 2024 paper Protein Discovery with Discrete Walk-Jump Sampling received an ICLR Outstanding Paper Award, and his 2025 NYU thesis developed methods to generate and score protein sequences. As of October 2026, he is a founder of Mythos Scientific and coauthors MetaNCA, which learns local rules to self-organize neural network weights.

Now at
Mythos Scientific
Badges

Timeline

  1. 2017–2019

    Mathematics and CS at Vermont

    He earns a BS in mathematics at the University of Vermont in 2017 and an MS in computer science there in 2019.

  2. By 2021

    Protein modeling at Flatiron

    At the Flatiron Institute, he works as a research analyst in Richard Bonneau’s systems biology group, exploring protein structure and function prediction with self- and semi-supervised deep learning. In October 2020, he presents protein structure embeddings through contact map decomposition and convolution at a Simons Foundation Systems Biology Group Meeting.

  3. 2021

    Genentech and DeepFRI

    In 2021, he joins Genentech with Prescient Design as part of its acquisition and develops usable code for deep computational protein design. He coauthors DeepFRI, a graph convolutional network that predicts protein functions from sequence features extracted from a protein language model and protein structures.

  4. 2022

    Sampling antibody sequences

    In 2022, he coauthors multi-segment preserving sampling for antibody design; the method preserves selected sequence segments while allowing variation in others.

  5. 2023

    Deep protein homology methods

    In 2023, he coauthors TM-Vec and DeepBLAST, deep learning methods for finding remote protein homology and structurally aligning proteins from sequence information.

  6. 2024

    An ICLR Outstanding Paper Award

    In 2024, he coauthors Protein Discovery with Discrete Walk-Jump Sampling, a discrete generative method for antibody proteins. The paper is one of five ICLR 2024 Outstanding Paper winners.

  7. 2025

    A thesis on protein design

    His 2025 NYU PhD thesis, Modern machine learning methods for protein design, develops methods to generate and score protein sequences, including text-conditioned models. Richard Bonneau and Kyunghyun Cho advise the thesis.

  8. As of October 2026

    A founder at Mythos Scientific

    In July 2026, he coauthors Architecture Generalization with MetaNCA, a method that learns local rules to self-organize artificial neural-network weights and generalizes to architectures unseen during training. As of October 2026, he is a founder of Mythos Scientific, an AI/ALife research company focused on artificial neural networks that grow and self-organize.

Career

  1. NowMythos ScientificFounder
  2. From 2021Roche
  3. Simons FoundationResearch analyst

Education

  1. 2025New York UniversityPhD
  2. 2019University of VermontMS
  3. 2017University of VermontBS

From the biography.

  1. 23% of people shaping AI who left a university for a company later returned to a universityIncludes Dan Berenberg. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  2. Direct Form 990 pay in the latest returns of 39 nonprofits with AI people on this site is highest at $2.1M at the Allen Institute for AI (Ai2) (2024)Includes Simons Foundation. IRS Form 990 compensation across 39 nonprofits with AI people on this site from 2012 to 2025: in the latest returns, Mozilla reports $6.1M including related-company pay (2024) and the Allen Institute for AI (Ai2) leads direct nonprofit pay at $2.1M (2024).
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Roche. 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.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Roche. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

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

Sources and credits

Photo: We have no published photo of Dan Berenberg yet. Send us a free-licensed photo.

  1. Genentech: Daniel Berenberg | Machine Learning Engineer, Prescient Design, Computational Sciences gene.com. Checked 22 October 2026.
  2. Microbiome Immunity Project | Research | World Community Grid worldcommunitygrid.org. Checked 22 October 2026.
  3. Systems Biology Group Meeting simonsfoundation.org. Checked 22 October 2026.
  4. Structure-based protein function prediction using graph convolutional networks nature.com. Checked 22 October 2026.
  5. Multi-segment preserving sampling for deep manifold sampler arxiv.org. Checked 22 October 2026.
  6. Protein remote homology detection and structural alignment using deep learning nature.com. Checked 22 October 2026.
  7. Protein Discovery with Discrete Walk-Jump Sampling proceedings.iclr.cc. Checked 22 October 2026.
  8. ICLR 2024 Outstanding Paper Awards blog.iclr.cc. Checked 22 October 2026.
  9. NYU Computer Science Department cs.nyu.edu. Checked 22 October 2026.
  10. Architecture Generalization with MetaNCA arxiv.org. Checked 22 October 2026.
  11. Mythos Scientific mythos.science. Checked 22 October 2026.