Sotabase

Career

· Founding Scientist, Head of Machine Learning, Atomic AI2021–
· Bio-X Travel Awardee, Stanford University2020–2021
· Bio-X Bowes Fellow, Stanford University2019–2021
· PhD in Applied Physics, Stanford University2017–2021

Publications (55)

Learning from Protein Structure with Geometric Vector Perceptrons
International Conference on Learning Representations · 2020
595
cited
High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor
Science · 2015
432
cited
Geometric deep learning of RNA structure
Science · 2021
314
cited
Molecular mechanism of biased signaling in a prototypical G protein–coupled receptor
Science · 2020
209
cited
ATOM3D: Tasks On Molecules in Three Dimensions
NeurIPS Datasets and Benchmarks · 2020
154
cited
Learning Neural PDE Solvers with Convergence Guarantees
International Conference on Learning Representations · 2019
154
cited
Equivariant Graph Neural Networks for 3D Macromolecular Structure
arXiv.org · 2021
116
cited
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes
Proteins: Structure, Function, and Bioinformatics · 2020
64
cited
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Proteins: Structure, Function, and Bioinformatics · 2021
37
cited
Bayesian optimization and attribute adjustment
Conference on Uncertainty in Artificial Intelligence · 2018
35
cited
RNA-Puzzles Round V: blind predictions of 23 RNA structures
Nature Methods · 2024
35
cited
A preclinical microbeam facility with a conventional x‐ray tube
Medical Physics (Lancaster) · 2016
23
cited
ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data
bioRxiv · 2023
13
cited
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
arXiv.org · 2020
13
cited
Shape optimization in laminar flow with a label-guided variational autoencoder
arXiv.org · 2017
13
cited
Geometric Prediction: Moving Beyond Scalars
arXiv.org · 2020
9
cited
Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes
arXiv.org · 2020
8
cited
Protein Connectivity in Chemotaxis Receptor Complexes
PLoS Comput. Biol. · 2015
7
cited
Protein model quality assessment using rotation‐equivariant transformations on point clouds
Proteins: Structure, Function, and Bioinformatics · 2023
4
cited
ARES-specific adaptation of E3NN
2021
1
cited
Sotabase
Stephan Eismann | Researcher Profile | Sotabase | Sotabase