Biography
Tristan Bepler is the Group Leader of the Simons Machine Learning Center. Tristan received his undergraduate degree from Duke University in Computer Science and Biology. He received his PhD from MIT in the Computational and Systems Biology Program under the supervision of Bonnie Berger. He joined NYSBC to lead the SMLC after a postdoc with Tim Lu at MIT. His research interests broadly encompass machine learning methods for better understanding protein sequence, structure, and function as well as machine learning methods for protein structure determination especially with cryoEM and cryoET.
Publications
Ghostbuster: Beyond Ewald Sphere Curvature Correction for Cryo-EM Single Particle Reconstruction Via Phase Retrieval
Ghostbuster: Beyond Ewald Sphere Curvature Correction for Cryo-EM Single Particle Reconstruction Via Phase Retrieval
Ghostbuster: A phase retrieval diffraction tomography algorithm for cryo-EM
Ultramicroscopy, Vol. 262, Pages 113962
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Nature Communications, Vol. 14, Issue 1
Learning to automate cryo-electron microscopy data collection with Ptolemy
IUCrJ, Vol. 10, Issue 1, Pages 90-102
Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries
An engineered protein-phosphorylation toggle network with implications for endogenous network discovery
Science, Vol. 373, Issue 6550
CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
Nature Methods, Vol. 18, Issue 2, Pages 176-185
Topaz-Denoise: general deep denoising models for cryoEM and cryoET
Nature Communications, Vol. 11, Issue 1
CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks
Topaz-Denoise: general deep denoising models for cryoEM
Explicitly disentangling image content from translation and rotation with spatial-VAE
Neural Information Processing Systems
Visualization of clustered protocadherin neuronal self-recognition complexes.
Nature, Vol. 569, Issue 7755, Pages 280-283
Learning protein sequence embeddings using information from structure
International Conference on Learning Representations
Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs.
Research in computational molecular biology, Vol. 16, Issue 11, Pages 1153-1160
Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding.
Cell systems, Vol. 6, Issue 4, Pages 470-483.e8
Distinct routes to metastasis: plasticity-dependent and plasticity-independent pathways.
Oncogene, Vol. 35, Issue 33, Pages 4302-4311
Human-Chimpanzee Differences in a {FZD8} Enhancer Alter Cell-Cycle Dynamics in the Developing Neocortex
Current Biology, Vol. 25, Issue 6, Pages 772-779