| De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... Nature 620 (7976), 1089-1100, 2023 | 1211* | 2023 |
| Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem BL Trippe, J Yim, D Tischer, D Baker, T Broderick, R Barzilay, T Jaakkola arXiv preprint arXiv:2206.04119, 2022 | 253 | 2022 |
| SE (3) diffusion model with application to protein backbone generation J Yim, BL Trippe, V De Bortoli, E Mathieu, A Doucet, R Barzilay, ... arXiv preprint arXiv:2302.02277, 2023 | 213 | 2023 |
| Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases EM Weeks, JC Ulirsch, NY Cheng, BL Trippe, RS Fine, J Miao, ... Nature Genetics 55 (8), 1267-1276, 2023 | 211 | 2023 |
| Conditional density estimation with bayesian normalising flows BL Trippe, RE Turner arXiv preprint arXiv:1802.04908, 2018 | 76 | 2018 |
| Practical and asymptotically exact conditional sampling in diffusion models L Wu, B Trippe, C Naesseth, D Blei, JP Cunningham Advances in Neural Information Processing Systems 36, 31372-31403, 2023 | 66 | 2023 |
| Overpruning in variational bayesian neural networks B Trippe, R Turner arXiv preprint arXiv:1801.06230, 2018 | 54 | 2018 |
| The kernel interaction trick: Fast Bayesian discovery of pairwise interactions in high dimensions R Agrawal, B Trippe, J Huggins, T Broderick International Conference on Machine Learning, 141-150, 2019 | 32 | 2019 |
| Many processors, little time: MCMC for partitions via optimal transport couplings TD Nguyen, BL Trippe, T Broderick International Conference on Artificial Intelligence and Statistics, 3483-3514, 2022 | 18* | 2022 |
| Gaussian processes at the Helm (holtz): A more fluid model for ocean currents R Berlinghieri, BL Trippe, DR Burt, R Giordano, K Srinivasan, ... arXiv preprint arXiv:2302.10364, 2023 | 13 | 2023 |
| Randomized gates eliminate bias in sort‐seq assays BL Trippe, B Huang, EA DeBenedictis, B Coventry, N Bhattacharya, ... Protein Science 31 (9), e4401, 2022 | 13 | 2022 |
| LR-GLM: High-dimensional Bayesian inference using low-rank data approximations B Trippe, J Huggins, R Agrawal, T Broderick International conference on machine learning, 6315-6324, 2019 | 13 | 2019 |
| Inhibition of cell fate repressors secures the differentiation of the touch receptor neurons of Caenorhabditis elegans C Zheng, FQ Jin, BL Trippe, J Wu, M Chalfie Development 145 (22), dev168096, 2018 | 13 | 2018 |
| Neural network for processing aptamer data MTH Dimon, M Berndl, MA Coram, B Trippe, PF Riley, PC Nelson US Patent 10,546,650, 2020 | 7 | 2020 |
| Practical and asymptotically exact conditional sampling in diffusion models BL Trippe, L Wu, CA Naesseth, D Blei, JP Cunningham ICML 2023 Workshop on Structured Probabilistic Inference {\&} Generative …, 2023 | 5 | 2023 |
| For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets B Trippe, H Finucane, T Broderick Advances in Neural Information Processing Systems 34, 13471-13484, 2021 | 3 | 2021 |
| Confidently Comparing Estimates with the c-value BL Trippe, SK Deshpande, T Broderick Journal of the American Statistical Association 119 (546), 983-994, 2024 | 2 | 2024 |
| K-mer Motif Multinomial Mixtures, a scalable framework for multiple motif discovery BL Trippe, S Prabhakaran, HJ Bussemaker bioRxiv, 096735, 2016 | 1 | 2016 |
| MotifBench: A standardized protein design benchmark for motif-scaffolding problems Z Zheng, B Zhang, K Didi, KK Yang, J Yim, JL Watson, HF Chen, ... arXiv preprint arXiv:2502.12479, 2025 | | 2025 |
| Neural network for processing aptamer data MTH Dimon, M Berndl, MA Coram, B Trippe, PF Riley, PC Nelson US Patent App. 18/307,748, 2024 | | 2024 |
Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
