PhD candidate

University of Amsterdam

I am a third-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.

- One accepted paper at UAI 2018: Hyperspherical Variational Auto-Encoders and a short paper at MIDL 2018 on Extraction of Airways using Graph Neural Networks
- Our work on learning latent interaction graphs: Neural Relational Inference for Interacting Systems is accepted as a conference paper at ICML 2018.
- I will be joining Google DeepMind for a summer internship in 2018.
- Our paper Modeling Relational Data with Graph Convolutional Networks is accepted as a conference paper at ESWC 2018.
- In the summer of 2017, I interned at Apple in Carlos Guestrin's Machine Learning team. I worked on self-attention models for sequence tagging and classification.

Learning the latent interaction graph of a dynamical system

T. Kipf*, E. Fetaya*, K. Wang, M. Welling, R. Zemel, Neural Relational Inference for Interacting Systems, (ICML 2018) [Link, PDF (arXiv), code], *equal contribution.

Learning hyperspherical latent spaces

T. R. Davidson*, L. Falorsi*, N. De Cao*, T. Kipf, J. M. Tomczak, Hyperspherical Variational Auto-Encoders, (UAI 2018) [Link, PDF (arXiv), code, blog], *equal contribution.

Graph refinement with graph neural nets

R. Selvan, T. Kipf, M. Welling, J. H. Pedersen, J. Petersen, M. de Bruijne, Extraction of Airways using Graph Neural Networks, (MIDL 2018 - short paper) [Link, PDF (arXiv)].

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, (ESWC 2018) [Link, PDF (arXiv), code], *equal contribution.

Collaborative filtering with side information in bipartite graphs

R. vd Berg, T. N. Kipf, M. Welling, Graph Convolutional Matrix Completion, arXiv:1706.02263 (2017) [Link, PDF (arXiv), code]

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]

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), code]

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

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

For a full list, have a look at my Google Scholar page.