Daniel Zügner

Microsoft (Germany) — Germany

Computer Science · Physical Sciences

16h-index2.8kcitations41works83.02yr avg
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Research Topics

Adversarial Robustness in Machine Learning(21), Advanced Graph Neural Networks(16), Anomaly Detection Techniques and Applications(12), Machine Learning in Materials Science(5), Complex Network Analysis Techniques(4)

Publications41 total

Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning
arXiv (Cornell University)·2025Open Access
A generative model for inorganic materials design
Nature·2025· 264 citedOpen Access
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
arXiv (Cornell University)·2024· 68 citedOpen Access
Expected Probabilistic Hierarchies
2024
MatterGen: a generative model for inorganic materials design
arXiv (Cornell University)·2023· 67 citedOpen Access
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Journal of Chemical Theory and Computation·2023· 77 citedOpen Access
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
arXiv (Cornell University)·2023· 10 citedOpen Access
Training Differentially Private Graph Neural Networks with Random Walk Sampling
arXiv (Cornell University)·2023· 3 citedOpen Access
On the Robustness and Anomaly Detection of Sparse Neural Networks
arXiv (Cornell University)·2022· 2 citedOpen Access
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
arXiv (Cornell University)·2022· 4 citedOpen Access
Monte Carlo EM for Deep Time Series Anomaly Detection
arXiv (Cornell University)·2021· 3 citedOpen Access
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
arXiv (Cornell University)·2021· 9 citedOpen Access
Robustness of Graph Neural Networks at Scale
arXiv (Cornell University)·2021· 4 citedOpen Access
Graph Posterior Network: Bayesian Predictive Uncertainty for Node\n Classification
arXiv (Cornell University)·2021· 24 citedOpen Access
A Study of Joint Graph Inference and Forecasting
arXiv (Cornell University)·2021· 4 citedOpen Access
A Study of Joint Graph Inference and Forecasting
arXiv (Cornell University)·2021· 5 citedOpen Access
On Out-of-distribution Detection with Energy-based Models
arXiv (Cornell University)·2021· 2 citedOpen Access
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions.
arXiv (Cornell University)·2021· 12 citedOpen Access
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
arXiv (Cornell University)·2021· 3 citedOpen Access
Language-Agnostic Representation Learning of Source Code from Structure and Context
arXiv (Cornell University)·2021· 63 citedOpen Access
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Frequent Co-authors

Stephan Günnemann(34), Bertrand Charpentier(13), Simon Geisler(6), Amir Akbarnejad(5), Robert Pinsler(5), Aleksandar Bojchevski(5), Claudio Zeni(4), Matthew K. Horton(4), Simon Geisler(4), Jake A. Smith(3), Yichi Zhou(3), Oleksandr Shchur(3), Oliver Borchert(3), Victor García Satorras(3), François-Xavier Aubet(3), Jan Gasthaus(3), Morgane Ayle(3), Andrew T. Fowler(2), Jonathan Crabbé(2), Chin‐Wei Huang(2)