Research Scientist

Google Research

I am a Research Scientist at Google Research in the Brain Team in Amsterdam. Previously, I obtained my PhD on "Deep Learning with Graph-Structured Representations" (thesis available here) at the University of Amsterdam under the supervision of Prof. Max Welling. I am broadly interested in developing and studying machine learning models that can reason about the rich structure of the physical world and its combinatorial complexity. This includes topics in graph representation learning, object-centric learning, and causal representation learning.

- 12/2020: I am an Area Chair at ICLR.
- 09/2020: Our paper on Object-centric Learning with Slot Attention is accepted for spotlight presentation at NeurIPS!
- 07/2020: I gave two invited workshop talks at ICML: Attentive Grouping and Relational Structure Discovery.
- 04/2020: I have defended my PhD thesis with distinction "cum laude" (awarded 3x in the past 10 years at our institute). My thesis on "Deep Learning with Graph-Structured Representations" is available here.
- 01/2020: I have joined Google Brain as a Research Scientist in Amsterdam.
- 12/2019: I am co-organizing the ELLIS Workshop on Geometric and Relational Deep Learning.
- 12/2019: Our work on Contrastive Learning of Structured World Models is accepted at ICLR 2020 as an oral presentation.
- 08/2019: I am co-organizing the Graph Representation Learning workshop at NeurIPS 2019.
- 05/2019: I gave a tutorial on Unsupervised Learning with Graph Neural Networks at the UCLA IPAM Workshop on Deep Geometric Learning of Big Data (slides, video).
- 04/2019: Our work on Compositional Imitation Learning is accepted at ICML 2019 as a long oral.
- 03/2019: I am co-organizing two workshops: Representation Learning on Graphs and Manifolds (ICLR 2019) and Learning and Reasoning with Graph-Structured Data (ICML 2019).
- 06/2018: Our work on Relational GCNs has won the best student research paper award at ESWC 2018.

Iterative, competitive attention for object discovery

F. Locatello*, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, T. Kipf*, Object-centric Learning with Slot Attention, (NeurIPS 2020), Spotlight [Link, PDF (arXiv)], *equal contribution.

PhD thesis (2020), University of Amsterdam

T. N. Kipf, Deep Learning with Graph-Structured Representations [Link]

Unsupervised discovery of objects, relations, and consequences of actions.

T. Kipf, E. van der Pol, M. Welling, Contrastive Learning of Structured World Models, (ICLR 2020), Oral [Link, PDF (arXiv)].

Unsupervised, differentiable sequence segmentation for option discovery.

T. Kipf, Y. Li, H. Dai, V. Zambaldi, A. Sanchez-Gonzales, E. Grefenstette, P. Kohli, P. Battaglia, CompILE: Compositional Imitation Learning and Execution, (ICML 2019), Long Oral [Link, PDF (arXiv)].

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 to generate molecular graphs using a combined GAN/RL-based objective

N. De Cao, T. Kipf, MolGAN: An implicit generative model for small molecular graphs, ICML Deep Generative Models Workshop (2018) [Link, PDF (arXiv), code].

Learning hyperspherical latent spaces

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

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), Best Student Research Paper [Link, PDF (arXiv), code], *equal contribution.

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, (NeurIPS Bayesian Deep Learning Workshop 2016) [Link, PDF (arXiv), code]

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