Publications

* denotes equal contribution

  1. Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

    Andrew Campbell*, Jason Yim*, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola

    International Conference on Machine Learning
    July 21, 2024

    Paper Code
  2. Diffusion models in protein structure and docking

    Jason Yim, Hannes Stärk, Gabriele Corso, Bowen Jing, Regina Barzilay, and Tommi S Jaakkola

    Wiley Interdisciplinary Reviews: Computational Molecular Science
    Apr 5, 2024

    Paper
  3. Improving protein optimization with smoothed fitness landscapes

    Andrew Kirjner*, Jason Yim*, Raman Samusevich, Shahar Bracha, Tommi Jaakkola, Regina Barzilay, and Ila Fiete

    International Conference on Learning Representations
    May 7, 2024

    Paper Code NEWS
  4. Improved motif-scaffolding with SE (3) flow matching

    Jason Yim, Andrew Campbell, Emile Mathieu, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Frank Noé, and  others

    arXiv (under review)
    Jan 8, 2024

    Paper Code
  5. De novo design of protein structure and function with RFdiffusion

    Joseph L. Watson*, David Juergens*, Nathaniel R. Bennett*, Brian L. Trippe*, Jason Yim*, Helen E. Eisenach*, Woody Ahern*, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker

    Nature
    Jul 11, 2023
    Image credit: Ian Haydon of Institute for Protein Design

    Paper Code NEWS
  6. SE (3) diffusion model with application to protein backbone generation

    Jason Yim*, Brian L Trippe*, Valentin De Bortoli*, Emile Mathieu*, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola

    International Conference of Machine Learning
    Feb 5, 2023

    Paper Code NEWS
  7. Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

    Brian L. Trippe*, Jason Yim*, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola

    International Conference on Learning Representations
    May 1, 2023

    Paper Talk
  8. Protein complex prediction with AlphaFold-Multimer

    Richard Evans, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žı́dek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian Bodenstein, Michal Zielinski, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, and Demis Hassabis

    bioRxiv
    Mar 10, 2022

    Paper Code
  9. Predicting conversion to wet age-related macular degeneration using deep learning

    Jason Yim*, Reena Chopra*, Terry Spitz, Jim Winkens, Annette Obika, Christopher Kelly, Harry Askham, Marko Lukic, Josef Huemer, Katrin Fasler, and  others

    Nature Medicine
    May 18, 2020

    Paper Talk Code Blog
  10. JMLR
    Sparse Projection Oblique Randomer Forests

    Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, and Joshua T. Vogelstein

    Journal of Machine Learning Research
    May 8, 2020

    Paper Code