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Brian Trippe
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.
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.
Brian L Trippe,
Sameer K Deshpande,
Tamara Broderick
(2023).
Confidently Comparing Estimates with the c-value.
The Journal of the American Statistical Association.
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.
Recent Publications
Quickly discover relevant content by filtering 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.
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.
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.
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.
Brian L Trippe,
Sameer K Deshpande,
Tamara Broderick
(2023).
Confidently Comparing Estimates with the c-value.
The Journal of the American Statistical Association.
Contact
- <first initial> <last name> at stanford dot edu
- Stanford, CA https://statistics.stanford.edu/