Thomas Kipf

PhD candidate

University of Amsterdam

I am a second-year PhD student in Deep Learning for Network Analysis at the University of Amsterdam, supervised by Prof. Max Welling. My main area of interest is large-scale inference for structured data and semi-supervised learning. I am also interested in reasoning and multi-agent reinforcement learning.

My formal background is in Physics (M.Sc. hons. 2016, B.Sc. 2014 at FAU). During my studies, I have had exposure to a number of fields and—after a short interlude in Neuroscience-related research at the Max Planck Institute for Brain Research—eventually developed a deep interest in Machine Learning.

Projects / Publications


Modeling Relational Data with Graph Convolutional Networks

Link prediction and entity classification on knowledge graphs

M. Schlichtkrull*, T. N. Kipf*, P. Bloem, R. vd Berg, I. Titov, M. Welling, Modeling Relational Data with Graph Convolutional Networks, arXiv:1703.06103 (2017) [Link, PDF (arXiv)], *equal contribution.


Semi-Supervised Classification with Graph Convolutional Networks

Neural networks for node classification on graphs

T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) [Link, PDF (arXiv), code, blog]


Variational Graph Auto-Encoders

A latent variable model for graph-structured data

T. N. Kipf, M. Welling, Variational Graph Auto-Encoders, (NIPS Bayesian Deep Learning Workshop 2016) [Link, PDF (arXiv)]


Recurrent Neural Networks for Graph-Based 3D Agglomeration

M.Sc. thesis (2016), Department of Connectomics, MPI for Brain Research

Thesis copy available here: [PDF (9 MB)]


Collective Effects in Multimode Quantum Optomechanics

Research project in Theoretical Quantum Optics group at OSU

T. Kipf, G. S. Agarwal, Superradiance and collective gain in multimode optomechanics, Phys. Rev. A 90, 053808 (2014) [Link, PDF (arXiv)]

Curriculum vitae