Brian Trippe

Brian Trippe

Postdoctoral Fellow

Columbia University

About me

I am 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. 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.

Interests
  • Probabilistic Machine Learning
  • Bayesian Computation
  • Computational Biology
  • Protein Engineering
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

Recent Publications

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(2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. Neural Information Processing Systems.

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(2023). Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nature Genetics.

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(2023). De novo design of protein structure and function with RFdiffusion. Nature.

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(2023). Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents. International Conference on Machine Learning.

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(2023). Confidently Comparing Estimates with the c-value. The Journal of the American Statistical Association.

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