Brian Trippe

Brian Trippe

Assistant Professor

Stanford University

About me

I am an assistant professor at Stanford in the Department of Statistics, with an affiliation in Stanford Data Science.

In my research I develop probabilistic machine learning methods to address challenges in biotechnology and medicine. Recently, my focus has been on generative modeling and inference algorithms for protein engineering.

Before joining Stanford, I was a postdoctoral fellow at Columbia University in the Department of Statistics, and a visiting researcher at the Institute for Protein Design at the University of Washington.

Interested in working with me?

  • Current / admitted students at Stanford: feel free to reach out directly.
  • Prospective postdocs / visitors : email me (1) a few sentences on your research interests, (2) your CV, (3) a PDF of your most relevant prior work, and (4) contact information for two or more references.
  • If you are a prospective PhD student, consider applying to statistics. I can also advise students in other departments.

I especially welcome contacts from people who aren’t also white men.

Interests
  • Probabilistic Machine Learning
  • Bayesian Computation
  • Computational Biology
  • Protein Design
Education

  • PhD in Computational and Systems Biology, 2022

    Massachusetts Institute of Technology

  • MPhil in Engineering, 2017

    University of Cambridge

  • BA in Biochemistry, BA in Computer Science, 2016

    Columbia University

Selected Publications

Luhuan Wu, Brian L Trippe, Christian A Naesseth, David M Blei, John P Cunningham (2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. Neural Information Processing Systems.

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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, Sergey Ovchinnikov, Regina Barzilay, Tommi S Jaakkola, Frank DiMaio, Minkyung Baek, David Baker (2023). De novo design of protein structure and function with RFdiffusion. Nature.

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Brian L Trippe, Sameer K Deshpande, Tamara Broderick (2023). Confidently Comparing Estimates with the c-value. The Journal of the American Statistical Association.

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Brian L Trippe, Buwei Huang, Erika A DeBenedictis, Brian Coventry, Nicholas Bhattacharya, Kevin K Yang, David Baker, Lorin Crawford (2022). Randomized gates eliminate bias in sort-seq assays. Protein Science.

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Recent Publications

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Luhuan Wu, Brian L Trippe, Christian A Naesseth, David M Blei, John P Cunningham (2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. Neural Information Processing Systems.

PDF Code Slides

Elle M Weeks, Jacob C Ulirsch, Nathan Y Cheng, Brian L Trippe, Rebecca S Fine, Jenkai Miao, Tejal A Patwardhan, Masahiro Kanai, Joseph Nasser, Charles P Fulco, Katherine C. Tashman, Francois Aguet, Taibo Li, Jose Ordovas-Montanes, Christopher S Smillie, Moshe Biton, Alex K Shalek, Ashwin N Ananthakrishnan, Ramnik J Xavier, Aviv Regev, Rajat M Gupta, Kasper Lage, Kristin G Ardlie, Joel N Hirschhorn, Eric S Lander, Jesse M Engreitz, Hilary K Finucane (2023). Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nature Genetics.

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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, Sergey Ovchinnikov, Regina Barzilay, Tommi S Jaakkola, Frank DiMaio, Minkyung Baek, David Baker (2023). De novo design of protein structure and function with RFdiffusion. Nature.

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Renato Berlinghieri, Brian L Trippe, David R Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick (2023). Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents. International Conference on Machine Learning.

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Brian L Trippe, Sameer K Deshpande, Tamara Broderick (2023). Confidently Comparing Estimates with the c-value. The Journal of the American Statistical Association.

PDF Code Slides

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