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Stephan Eismann

dblp:174/8729 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training
equivariant neural network
0.512021
Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021
Computer vision › 3D vision
geometric deep learning
0.512021
Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021
Bioinformatics and computational biology › protein analysis › protein bioinformatics
protein representation learning
0.512021
Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021
Bioinformatics and computational biology › structural bioinformatics
protein structure
0.512021
Learning from Protein Structure with Geometric Vector Perceptrons · ICLR 2021
Machine learning › Deep learning architectures and training › scientific machine learning
neural PDE solvers
0.412019
Learning Neural PDE Solvers with Convergence Guarantees · ICLR (Poster) 2019
Computational science and engineering
scientific machine learning
0.412019
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
YearPublicationVenuePosition
2021 Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing 0002, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, Ron O. Dror
ICLR2
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
UAI1
2015 Protein Connectivity in Chemotaxis Receptor Complexes
abstract
The 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