Tom Schaul is a Senior Staff Research Scientist at DeepMind with 18 years of experience advancing modular reinforcement learning, black-box optimization, and recurrent neural networks across domains from vision and neural decoding to games and robotics. He holds an MSc from EPFL and a PhD from TU Munich under Jürgen Schmidhuber, followed by a postdoc with Yann LeCun at NYU, a trajectory that blends deep theoretical foundations with high-impact industry research. At DeepMind since 2013 he has progressed through research ranks, driving work on meta-learning and artificial curiosity while maintaining hands-on coding contributions to open-source projects like PyBrain. Based in London and originally from Luxembourg, he combines a European academic pedigree with practical experience in production-scale AI research teams. An underappreciated thread in his profile is sustained focus on modularity—structuring learning systems so they generalize and adapt—which informs both his research and engineering decisions.
18 years of coding experience
7 years of employment as a software developer
University of Waterloo
MSc, MSc at EPFL (École polytechnique fédérale de Lausanne)
Dr. rer. nat., Dr. rer. nat. at Technische Universität München / Technical University of Munich
MSc, MSc at Columbia University
Baccalaureate, Baccalaureate at Lycée Classique Diekirch
Contributions:1 release, 402 commits, 15 PRs in 9 years 9 months
Contributions summary:Tom's commits focused on reducing logging messages and warnings within the PyBrain library. They made file-related modifications in the pybrain/utilities.py and pybrain/tools.py files. Their work involved the development of utility functions.
A video game description language (VGDL) built on top pf pygame.
Contributions:261 commits, 2 PRs, 2 pushes in 7 years 1 month
pythonpygamegame-developmentvideo-gamevideo
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