Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephan Eismann
dblp:174/8729
· DBLP profile ↗
4ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Deep learning architectures and training · 64% 3D vision · 36% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 72% Computational science and engineering · 28% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training
equivariant neural network |
0.5 | 1 | 2021 | Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021 |
Computer vision › 3D vision
geometric deep learning |
0.5 | 1 | 2021 | Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021 |
Bioinformatics and computational biology › protein analysis › protein bioinformatics
protein representation learning |
0.5 | 1 | 2021 | Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021 |
Bioinformatics and computational biology › structural bioinformatics
protein structure |
0.5 | 1 | 2021 | Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021 |
Machine learning › Deep learning architectures and training › scientific machine learning
neural PDE solvers |
0.4 | 1 | 2019 | Learning Neural PDE Solvers with Convergence Guarantees · ICLR (Poster) 2019 |
Computational science and engineering
scientific machine learning |
0.4 | 1 | 2019 | Learning Neural PDE Solvers with Convergence Guarantees · ICLR (Poster) 2019 |
Methods — techniques the papers use, named apart from their topics
geometric vector perceptron · 1.0neural network · 0.8equivariant networks · 0.5equivariant network · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing 0002, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, Ron O. Dror |
ICLR | 2 |
| 2019 | Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon |
ICLR (Poster) | 3 |
| 2018 | Bayesian optimization and attribute adjustment
Stephan Eismann, Daniel Levy 0002, Stefan Bartzsch, Stefano Ermon |
UAI | 1 |
| 2015 | Protein Connectivity in Chemotaxis Receptor ComplexesabstractThe chemotaxis sensory system allows bacteria such as Escherichia coli to swim towards nutrients and away from repellents. The underlying pathway is remarkably sensitive in detecting chemical gradients over a wide range of ambient concentrations. Interactions among receptors, which are predominantly clustered at the cell poles, are crucial to this sensitivity. Although it has been suggested that the kinase CheA and the adapter protein CheW are integral for receptor connectivity, the exact coupling mechanism remains unclear. Here, we present a statistical-mechanics approach to model the receptor linkage mechanism itself, building on nanodisc and electron cryotomography experiments. Specifically, we investigate how the sensing behavior of mixed receptor clusters is affected by variations in the expression levels of CheA and CheW at a constant receptor density in the membrane. Our model compares favorably with dose-response curves from in vivo Förster resonance energy transfer (FRET) measurements, demonstrating that the receptor-methylation level has only minor effects on receptor cooperativity. Importantly, our model provides an explanation for the non-intuitive conclusion that the receptor cooperativity decreases with increasing levels of CheA, a core signaling protein associated with the receptors, whereas the receptor cooperativity increases with increasing levels of CheW, a key adapter protein. Finally, we propose an evolutionary advantage as explanation for the recently suggested CheW-only linker structures. Stephan Eismann, Robert G. Endres |
PLoS Comput. Biol. | 1 |