Thomas Kipf

PhD candidate

University of Amsterdam

I am a first 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 self-organization and learning in biological systems.

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

Variational Graph Auto-Encoders

A latent variable model for graph-structured data

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

Semi-Supervised Classification with Graph Convolutional Networks

First steps towards large-scale applications with neural networks on graphs

T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, arXiv:1609.02907 (2016) [Link, PDF (arXiv), code, blog]

Recurrent Neural Networks for Graph-Based 3D Agglomeration

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

Thesis copy provided upon request.

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)]

Quantum State Reconstruction of Spatially Multimode Fields

B.Sc. thesis (2014), Institute for Optics, Information and Photonics, FAU

Thesis copy provided upon request.

Curriculum vitae