OATML

Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and a Turing Fellow at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. Prior to his move to Oxford he was a Research Fellow in Computer Science at St Catharine’s College at the University of Cambridge. He obtained his PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. Prior to that he studied at Oxford Computer Science department for a Master’s degree under the supervision of Prof Phil Blunsom. Before his MSc he worked as a software engineer for 3 years at IDesia Biometrics developing code and UI for mobile platforms, and did his undergraduate in mathematics and computer science at the Open University in Israel.

Fields Yarin has published work in include: Bayesian deep learning • deep learning • adversarial machine learning • approximate Bayesian inference • Gaussian processes • Bayesian modelling • Bayesian non-parametrics • scalable MCMC • generative modelling. With applications including: AI safety • ML interpretability • reinforcement learning • active learning • natural language processing • computer vision • medical analysis. The arching theme leading his research is understanding empirically developed machine learning techniques. A full list of publications is available here.

Tom is a researcher in statistical machine learning at the University of Oxford. His research covers a wide range of topics including probabilistic programming, Monte Carlo methods, variational inference, experimental design, and deep generative models. He is a part-time senior postdoc with the OATML research group, and a junior research fellow at Christ Church college.

Zac is a Postdoc in OATML under Yarin Gal. His primary research interest is technical AI safety, and is currently working on robustness and safe exploration in reinforcement learning. He was previously a Research Assistant with Owain Evans at the Future of Humanity Institute, University of Oxford and a Visiting Researcher at the Montreal Institute for Learning Algorithms (MILA), under Yoshua Bengio. He also worked as a Data Scientist at ASI Data Science. He completed his PhD at Queen Mary University of London in Theoretical Physics, where he worked on string theory and cosmology. Prior to his PhD, he studied Mathematics at the University of Cambridge.

Challenger is a theoretical physicist working on the long-standing problem of quantising gravity. Recently, he started to undertake work in Theoretical Machine Learning, in order to exploit its tools to understand String Theory as a problem in big data. As a Rhodes Scholar he has pursued his passion in understanding fundamental physical processes by undertaking doctoral work in String Theory at the Rudolf Peierls Centre for Theoretical Physics, University of Oxford. During his recently completed thesis, he worked on understanding various aspects of complex geometries that feature in String Theory, using the tools of differential and algebraic-geometry. Prior to this, Challenger completed his undergraduate work in Physics from the Indian Institute of Science Education and Research, Kolkata. During his undergraduate years, he worked on a NASA-sponsored project applying stochastic optimisation techniques to analyse data from the proposed gravitaional wave detector, LISA. When he isn’t tied up in stringy or machine learning affairs, he likes to show off his constantly diminishing skills in table tennis.

Angelos is a DPhil student in the Department of Computer Science at the University of Oxford, where he works in the Applied and Theoretical Machine Learning group (OATML) under the supervision of Yarin Gal. His research interests span multi-agent systems, meta-learning and reinforcement learning. He obtained an undergraduate and master’s degree from the Department of Electrical and Electronic Engineering at Imperial College London. He also contracts with J.P. Morgan Artificial Intelligence Research group, working on generative models, distributional reinforcement learning and inverse reinforcement learning.

Aidan is a doctoral student of Yarin Gal and Yee Whye Teh at The University of Oxford. He leads a research group, called FOR.ai, focussing on providing resources, mentorship, and facilitating collaboration between academia and industry. Aidan’s research deals in understanding and improving neural networks and their applications. Previously, he worked with Geoffrey Hinton and Łukasz Kaiser on the Google Brain team. He obtained his Bachelors from The University of Toronto with supervision from Roger Grosse. He is an AI Fellow for Open Philanthropy and a Clarendon Scholar.

Andreas is a 1st-year DPhil with Yarin Gal in the AIMS program. He is interested in Bayesian Deep Learning, and ethics and safety in AI. Before joining OATML he worked at DeepMind in London as a research engineer and for Google/YouTube in Zurich as a software engineer. He studied computer science and maths at the Technical University in Munich. While originally from Romania, he grew up in Southern Germany. He likes schnitzel, sarmale, bouldering and running. He is a Clarendon Scholar.

Clare is a DPhil student at the University of Oxford working with Yarin Gal and Marta Kwiatkowska. She has previously worked on developing a stronger theoretical understanding of distributional reinforcement learning at Google Brain, and is broadly interested in theoretical foundations of machine learning. She obtained her undergraduate degree in mathematics and computer science at McGill University, and is a Rhodes Scholar.

Joost is a DPhil student in the OATML group in the Department of Computer Science at the University of Oxford, supervised by Yarin Gal and Yee Whye Teh. He is interested in representation learning, variational inference, and Bayesian methods. Furthermore he likes to play with new programming languages for ML, such as Swift and Julia. Previously, he spent two years at Twitter Cortex as part of the team that came out of the Magic Pony acquisition. He obtained his MSc. at the University of Amsterdam, working with Max Welling and Diederik Kingma. He is an Oxford-Google DeepMind scholar.

Lewis Smith is a DPhil student supervised by Yarin Gal. His main interests are in the reliability and robustness of machine learning algorithms, Bayesian methods, and the automatic learning of structure (such as invariances in the data). He is also a member of the AIMS CDT. Before joining OATML, he recieved his masters degree in physics from the University of Manchester.

Milad is a DPhil student in the Computer Science department at the University of Oxford, where he works in the Machine Learning Systems Group (OX-MLSYS) under the supervision of Nic Lane and Applied and Theoretical Machine Learning Group (OATML) with Yarin Gal. Prior to joining the University of Oxford, he was a Senior Software Engineer at Qualcomm Technologies in Cambridge, and Imagination Technologies in Bristol. He received his MSc in Digital Signal Processing from University of Bristol in 2011. Milad is a member of Linacre College.

Seb is a DPhil student supervised by Yarin Gal and part of the Centre for Doctoral Training in Cyber Security. He is interested in the pragmatic fundamentals of deep learning for their own sake as well as for their application to safe and secure machine learning systems. Before joining the research group, he worked in technology policy (including biosafety and AI policy), social-entrepreneurship, and strategy consulting. He has been working on startups in the effective altruism community since 2012. He has a Masters degree in Physics and Philosophy from the University of Oxford.

Tim is a DPhil student in the Department of Computer Science at the University of Oxford, working with Yarin Gal and Yee Whye Teh. His research interests span Bayesian deep learning, variational inference, and reinforcement learning. Tim obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University, where he received the Charles E. Clark Memorial Award for Academic Excellence. He is an AI Fellow at Georgetown University’s Center for Security and Emerging Technology, a Fellow of the German Academic Scholarship Foundation, and a Rhodes Scholar.