I am a 3rd year Ph.D. student in Electrical Engineering and Computer Science at MIT, co-advised by Tommi Jaakkola and Bonnie Berger. I work on deep learning for structural biology and drug discovery, with a focus on generative models and molecular simulation.
Previously, I completed a B.S. in Computer Science at Stanford University. I was advised by Ron Dror and worked on equivariant neural networks for protein structure.
Previously, I completed a B.S. in Computer Science at Stanford University. I was advised by Ron Dror and worked on equivariant neural networks for protein structure.
Preprints
AlphaFold Meets Flow Matching for Generating Protein Ensembles
B Jing, B Berger, T Jaakkola
arXiv, 2024
[arxiv] [code]
Dirichlet Flow Matching with Applications to DNA Sequence Design
H Stärk,* B Jing,* B Berger, R Barzilay, T Jaakkola
arXiv, 2024
[arxiv] [code]
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
H Stärk, B Jing, R Barzilay, T Jaakkola
arXiv, 2023
[arxiv] [code]
Publications
Diffusion models in protein structure and docking
J Yim, H Stärk, G Corso, B Jing, R Barizilay, T Jaakkola
WIREs Computational Molecular Science, 2024
[doi]
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
B Jing, T Jaakkola, B Berger
ICLR, 2024
[openreview] [arxiv] [code]
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
G Corso,* H Stärk,* B Jing,* R Barzilay, T Jaakkola
ICLR, 2023
[openreview] [arxiv] [code] [news]
Torsional Diffusion for Molecular Conformer Generation
B Jing,* G Corso,* J Chang, R Barzilay, T Jaakkola
NeurIPS, 2022 (Oral)
[proceedings] [openreview] [arxiv] [code]
Subspace Diffusion Generative Models
B Jing,* G Corso,* R Berlinghieri, T Jaakkola
ECCV, 2022
[doi] [ecva.si] [arxiv] [code]
Protein Model Quality Assessment Using Rotation-Equivariant Transformations on Point Clouds
S Eismann,* P Suriana,* B Jing, R Townshend, R Dror
Proteins, 2023; 91: 1089–1096
[doi] [arxiv] [server] [code]
ATOM3D: Tasks On Molecules in Three Dimensions
R Townshend,* M Vögele,* P Suriana,* A Derry,* A Powers, Y Laloudakis, S Balachandar, B Jing, B Anderson, S Eismann, R Kondor, R Altman, R Dror
NeurIPS Datasets and Benchmarks, 2021 (Best Paper)
[proceedings] [openreview] [arxiv] [site] [docs] [code]
Learning from Protein Structure with Geometric Vector Perceptrons
B Jing,* S Eismann,* P Suriana, R Townshend, R Dror
ICLR, 2021 (Spotlight)
[openreview] [arxiv] [code]
Hierarchical, Rotation-Equivariant Neural Networks to Select Structural Models of Protein Complexes
S Eismann,* R Townshend,* N Thomas,* M Jagota, B Jing, R Dror
Proteins, 2021; 89: 493–501
[doi] [arxiv] [server]
Workshop Papers
Scalable Multimer Structure Prediction using Diffusion Models
P Pao-Huang, B Jing, B Berger
ML for Structural Biology Workshop @ NeurIPS, 2023
[workshop]
EigenFold: Generative Protein Structure Prediction with Diffusion Models
B Jing, E Erives, P Pao-Huang, G Corso, B Berger, T Jaakkola
ML for Drug Discovery Workshop @ ICLR, 2023
[workshop] [arxiv] [code]
Equivariant Graph Neural Networks for 3D Macromolecular Structure
B Jing, S Eismann, P Soni, R Dror
Workshop on Computational Biology @ ICML, 2021 (Best Spotlight Talk)
[workshop] [arxiv] [code]