PhD Candidate, Department of Computer Science, University of Oxford
Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom
tim.rudner [AT] cs.ox.ac.uk
I am a PhD Candidate in the Department of Computer Science at the University of Oxford, where I conduct research on probabilistic machine learning with Yarin Gal and Yee Whye Teh. My research interests span Bayesian deep learning, reinforcement learning, and variational inference. I am particularly interested in reinforcement learning as probabilistic inference, uncertainty quantification in deep learning, and probabilistic meta-learning. I am also a Rhodes Scholar.
I obtained an undergraduate degree in mathematics and economics from Yale University, where I received the Charles E. Clark Memorial Award for Academic Excellence and was fortunate to be advised by Sekhar Tatikonda. Subsequently, I earned a master's degree in statistics from the University of Oxford, where I was supervised by Dino Sejdinovic. Prior to starting my PhD, I conducted research on approximate inference in Deep Gaussian Process models, game theoretic equilibria in digital goods markets, and drivers of financial crises. I am a member of the Oxford Center for Doctoral Training in Autonomous Intelligent Machines & Systems (AIMS) and a Fellow of the German National Academic Foundation.
Mult3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery
Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski
AAAI Conference on Artificial Intelligence. 2019.
NeurIPS Workshop on AI for Social Good. 2018.
The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson
AAMAS International Conference on Autonomous Agents and Multiagent Systems. 2019.
On the Connection between Neural Processes and Gaussian Processes with Deep Kernels
Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal
NeurIPS Workshop on Bayesian Deep Learning. 2018.
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
NeurIPS Workshop on Probabilistic Reinforcement Learning and Structured Control. 2018.
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner and Dino Sejdinovic
NIPS Workshop on Bayesian Deep Learning. 2017.
A selection of classes I've TA'ed for in the past: