World's Best Scientists 2026 revealed!
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Computer Science
Netherlands
2026

D-Index & Metrics

Computer Science

D-Index
91
Citations
95028
World Ranking
554
National Ranking
5

Research.com Recognitions

  • 2026 - Research.com Computer Science in Netherlands Leader Award
  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2023 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award

Overview

Max Welling is affiliated with the University of Amsterdam in the Netherlands. Their research primarily falls within the field of Computer Science, with a strong emphasis on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics, and Molecular Biology.

The scientist's work covers several main topics, including:

  • Generative Adversarial Networks and Image Synthesis
  • Advanced Graph Neural Networks
  • Neural Networks and Applications
  • Model Reduction and Neural Networks
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning in Materials Science

Max Welling has contributed to various research publications in notable venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Nature
  • Dagstuhl Research Online Publication Server
  • Wiardi Beckman Foundation (Wiardi Beckman Foundation)
  • Computer

Recent papers authored by or involving Max Welling include:

  • Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories, 2025, Dagstuhl Research Online Publication Server
  • Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences, 2025, Wiardi Beckman Foundation (Wiardi Beckman Foundation)
  • Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach, 2025, Dagstuhl Research Online Publication Server
  • Scientific discovery in the age of artificial intelligence, 2023, Nature
  • A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence, 2020, Computer

They have collaborated extensively with several frequent co-authors, including Nicu Sebe, Herke van Hoof, Christos Louizos, Thomas Anderson Keller, and J. Brandstetter, reflecting a significant collaborative network.

Max Welling has authored books published by World Scientific and Morgan & Claypool Publishers. Notable titles include:

  • Equivariant and Coordinate Independent Convolutional Networks, 2024
  • Structured Representation Learning, 2025

Best Publications

  • Semi-Supervised Classification with Graph Convolutional Networks

    Thomas N. Kipf;Max Welling

  • Auto-Encoding Variational Bayes

    Diederik P Kingma;Max Welling

  • Modeling Relational Data with Graph Convolutional Networks

    Michael Sejr Schlichtkrull;Thomas N. Kipf;Peter Bloem;Rianne van den Berg

  • Semi-supervised Learning with Deep Generative Models

    Diederik P Kingma;Shakir Mohamed;Danilo Jimenez Rezende;Max Welling

  • An Introduction to Variational Autoencoders

    Diederik P. Kingma;Max Welling

  • Semi-Supervised Learning with Deep Generative Models

    Diederik P. Kingma;Danilo J. Rezende;Shakir Mohamed;Max Welling

  • Bayesian Learning via Stochastic Gradient Langevin Dynamics

    Max Welling;Yee W. Teh

  • Variational Graph Auto-Encoders

    Thomas N. Kipf;Max Welling

  • Improved Variational Inference with Inverse Autoregressive Flow

    Durk P. Kingma;Tim Salimans;Rafal Jozefowicz;Xi Chen

  • Graph Convolutional Matrix Completion

    Rianne van den Berg;Thomas N. Kipf;Max Welling

  • Variational dropout and the local reparameterization trick

    Diederik P. Kingma;Tim Salimans;Max Welling

  • Unsupervised Learning of Models for Recognition

    Markus Weber;Max Welling;Pietro Perona;Pietro Perona

  • Group equivariant convolutional networks

    Taco S. Cohen;Max Welling

  • Learning Sparse Neural Networks through L_0 Regularization

    Christos Louizos;Max Welling;Diederik P. Kingma

  • A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

    Yee W. Teh;David Newman;Max Welling

  • Attention-based Deep Multiple Instance Learning

    Maximilian Ilse;Jakub M. Tomczak;Max Welling

  • Fast collapsed gibbs sampling for latent dirichlet allocation

    Ian Porteous;David Newman;Alexander Ihler;Arthur Asuncion

  • On smoothing and inference for topic models

    Arthur Asuncion;Max Welling;Padhraic Smyth;Yee Whye Teh

  • Proceedings of the 26th International Conference on Neural Information Processing Systems

    C. J. C. Burges;L. Bottou;M. Welling;Z. Ghahramani

  • Attention, learn to solve routing problems!

    Wouter Kool;Herke van Hoof;Max Welling

  • Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1

    Z. Ghahramani;M. Welling;C. Cortes;N. D. Lawrence

  • Spherical CNNs

    Taco S. Cohen;Mario Geiger;Jonas Koehler;Max Welling

Frequent Co-Authors

Yee Whye Teh
Yee Whye Teh University of Oxford
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Padhraic Smyth
Padhraic Smyth University of California, Irvine
Pietro Perona
Pietro Perona California Institute of Technology
Richard S. Zemel
Richard S. Zemel University of Toronto
Efstratios Gavves
Efstratios Gavves University of Amsterdam
Kamalika Chaudhuri
Kamalika Chaudhuri University of California, San Diego
Marleen de Bruijne
Marleen de Bruijne Erasmus University Rotterdam
Kilian Q. Weinberger
Kilian Q. Weinberger Cornell University
Paul H. M. Savelkoul
Paul H. M. Savelkoul Maastricht University

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