Serie: Learning on Graphs Conference 2023

University of Oxford, Xiaowen Dong is an associate professor in the Department of Engineering Science at the University of Oxford, where he is a member of both the Machine Learning Research Group and the Oxford-Man Institute. Prior to joining Oxford, he was a postdoctoral associate in the MIT Media Lab, where he remains as a research affiliate, and received his PhD degree from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. His main research interests concern signal processing and machine learning techniques for analysing network data, and their applications in social and economic sciences.

Learning Types of Hierarchical Relationships on Complex Class Labelling Systems

Structure-Preserving Graph Neural Networks

ResolvNet: A Graph Convolutional Network with multi-scale Consistency

Robust Graph Neural Network based on Graph Denoising

Learning Graphs and Simplicial Complexes from Data

Technical University of Munich, Reinhard Heckel is a Rudolf Moessbauer assistant professor in the Department of Computer Engineering at the Technical University of Munich, and an adjunct assistant professor at Rice University, where he was an assistant professor in the ECE department from 2017-2019. Before that, he was a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and a researcher at the Cognitive Computing & Computational Sciences Department at IBM Research Zurich. He completed his PhD in electrical engineering in 2014 at ETH Zurich and was a visiting PhD student at the Statistics Department at Stanford University. Reinhard is working in the intersection of machine learning and signal/information processing with a current focus on deep networks for solving inverse problems, developing foundations and methods for machine learning, and DNA data storage.

Towards a more inductive world for drug repurposing approaches

Leveraging Temporal Graph Neural Networks for Drug Repurposing Using Prescription Data in Norway

A molecular foundation model for drug discovery based on molecular graphs

GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

A General Graph Convolution Theorem: Application to Dual Graph Inference

Tensor completion using graph-based diffusion: a remote sensing image completion study

Recovering Missing Node Features with Local Structure-based Embeddings

Completing Missing Air Quality Data with Graph-Based Techniques

Associate Professor at the Department of Mathematics and Computer Science of the University of Basel.

Using Graph Neural Networks to Predict Airport Congestion and Air Traffic Delays

Spatio-Temporal Graphs and GNN for Antimicrobial Multidrug Resistance Prediction in Intensive Care Unit

gMCSpy: Efficient and accurate computation of Genetic Minimal Cut Sets in Python

Bayesian Graph Neural Networks, how to optimize a cellular network and provide confidence to our customers

Asymmetry-Labeled DAGs: Representation, Learning and Causal Reasoning

HoloNets: Spectral Convolutions do extend to Directed Graphs

Graph-Based Dimensionality Reduction and Clustering for Earth and Life Sciences