Thomas Kipf
Senior Staff Research Scientist at Google DeepMind
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolThomas Kipf is a Senior Staff Research Scientist in San Francisco with a decade of experience at the intersection of generative models and visual understanding, currently leading new initiatives at Google DeepMind. He has driven teams and tech leadership for controllable generation and motion control, including featured work at Google I/O 2025, and has a track record of advancing object-centric image and video models. His background spans research roles across Google, DeepMind, and internships at Apple and the Max Planck Institute, built on a PhD in Computer Science from the University of Amsterdam and earlier physics degrees from FAU Erlangen-Nürnberg. An active contributor to scientific tooling, he enhanced a well-regarded single-cell best-practices repo with practical notebooks for pathway enrichment and visualization, showing fluency in applying ML to biological data. Colleagues know him for turning foundational research into demonstrable capabilities and for bridging rigorous theory with product-facing systems.
10 years of coding experience
6 years of employment as a software developer
Master of Science (M.Sc. hon.) Physics, Master of Science (M.Sc. hon.) Physics at FAU Erlangen-NürnbergDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Amsterdam
English, German
Github Skills (8)
cell10
rna-seq10
bioinformatics10
user-manual10
jupyter-notebook10
python10
data-analysis10
r9
Programming languages (3)
Github contributions (5)
theislab/single-cell-best-practices
Apr 2022 - Dec 2022
https://www.sc-best-practices.org
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 10 commits, 4 PRs in 8 months
Contributions summary:Thomas contributed significantly to the repository by adding and refining a Jupyter Notebook focused on gene set enrichment and pathway analysis within single-cell RNA-seq data. Their work included the development of introductory content, explanations of gene set test types, pathway and gene set collections, and technical considerations. The user also incorporated a case study demonstrating pathway enrichment analysis using a PBMC single-cell dataset and demonstrated how to visualize results.
in-progresssingle-cellwork-in-progressrna-seq
DavisLaboratory/mforoutan_tgfb_2017_tumours
Jan 2017 - Aug 2020
Contributions:1 release, 5 pushes, 1 branch in 3 years 8 months
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