Research Team

Pioneering intelligent systems, with scientific rigour

Overview

The DeepMind Research team brings together multidisciplinary, collaborative teams to develop cutting-edge AI research. By combining extraordinary intellectual freedom and scientific rigour with access to top resources and a structured, supportive culture, we have established an unparalleled track record of AI breakthroughs.

Our team

Our pioneering scientists and engineers have taught agents to cooperate, play world-class chess, diagnose eye disease, and predict the complex 3D shapes of proteins. Combined with a strong focus on safety, ethics, and robustness, the team works to create systems that can provide extraordinary benefits to society.

Research themes

We bring together knowledge from diverse disciplines to research the entire spectrum of intelligence from deep learning and neuroscience to robotics and safety.

Control & robotics

General purpose learning systems must be able to cope with the richness and complexity of the real world. These topics drive the control and robotics teams at DeepMind, which aim to create mechanical systems that can learn how to perform complex manipulation tasks with minimal prior knowledge. The shared ambition is to create systems that are data-efficient, reliable, and robust.

Read our control & robotics publications

Significant breakthroughs

We're proud of our track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.

AlphaZero: Shedding new light on chess, shogi, and Go

AlphaZero is a single system that learned to play three famously complex games, becoming the strongest player in history for each. Learning entirely from scratch, AlphaZero developed its own distinctive style that continues to inspire human grandmasters.

DQN: Human-level control of Atari games

One of the great challenges in AI is building flexible systems that can take on a wide range of tasks. Our Deep Q-Network (DQN) surpassed the overall performance of professional players in 49 different Atari games using only raw pixels and the score as inputs.

A neural network with dynamic memory

The differentiable neural computer (DNC) learns to use its external memory to answer questions about different kinds of complex structured data, such as artificially generated stories, family trees, or a map of the London Underground.

WaveNet: A generative model for raw audio

WaveNet can generate realistic human-sounding speech that reduced the gap between computer and human performance by over 50%, when it was introduced.

AlphaGo defeats Lee Sedol in the game of Go

In the course of winning, AlphaGo taught the world completely new knowledge about perhaps the most studied and contemplated game in history.

GQN: Learning to see

The Generative Query Network (GQN) allows computers to learn about a generated scene purely from observation, much like how infants learn to understand the world.

Team profile

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Raia Hadsell
Research Scientist

Raia worked in philosophy and religion before switching to machine learning and robotics. Her PhD from NYU focused on representation learning and robot navigation, using convolutional networks to see the world.

Raia’s team researches embodied and lifelong learning in complex situations, from robot locomotion to city-scale navigation and more.

"The pace of research progress at DeepMind is so fast it can be almost dizzying."

Team profile

Ali Eslami
Research Scientist

Ali holds a PhD in generative models from the University of Edinburgh, conducted his postdoc at Microsoft Research in Cambridge, and was a visiting researcher at the University of Oxford.

Ali figures out how computers can learn to see with less supervision. His work involves a mix of deep learning, probabilistic inference, and reinforcement learning.

"DeepMind is like a year-round conference, with a bit more focus."

Team profile

Jess Hamrick
Research Scientist

Jess holds a PhD in psychology from the University of California, Berkeley, and earned her BS and MEng in computer science from MIT.

Jess currently applies insights from cognitive science to problems in AI, with an emphasis on structured representations, model-based reasoning, and planning.

"As a cognitive scientist, it’s a unique place to work. DeepMind truly recognises that understanding human intelligence is a key path towards AGI.”

Team profile

Rich Sutton
Distinguished research scientist

Rich researched reinforcement learning at universities in Alberta and Massachusetts, and at corporate labs within AT&T and GTE, since 1978.

Rich works between DeepMind Alberta and the University of Alberta. He identifies unknown parts of the mind, which therefore prevent us from recreating its abilities in machines.

“DeepMind is the world’s leading organisation in artificial intelligence research. What an opportunity! What a responsibility!”

Team profile

Remi Munos 
Head of Paris team and integration team

Remi worked at Inria and taught at École Polytechnique. He did his postdoc at Carnegie Mellon University and holds a PhD on reinforcement learning. 

Remi focuses on deep reinforcement learning and combinations with unsupervised and imitation learning, and learning from a teacher.

“If we look at the world with a love of life, the world will reveal its beauty to us.” – Daisaku Ikeda

Team profile

Yazhe Li
Research Engineer

Yazhe studied theoretical and applied mechanics and civil engineering. She started her career as a civil engineer, but soon found her passion in computer science and machine learning.

Yazhe collaborates with research scientists on advancing our understanding of machine learning and developing state-of-the-art deep learning algorithms.

"DeepMind is a fantastic place to work, it fosters personal development and brings out the best in me."

Team profile

Hado van Hasselt
Research Scientist

Hado studied cognitive artificial intelligence and holds a PhD in AI from Utrecht University, NL. He later joined DeepMind after working with Professor Rich Sutton at the University of Alberta.

Hado builds systems and solves challenges with reinforcement learning, deep learning, and optimisation. He also co-leads an effort on core reinforcement learning algorithms.

"Learning algorithms can help solve problems you don’t yet have solutions for.”

Team profile

Jonathan Schwarz
Research Engineer

Jonathan earned a masters in machine learning from the University of Edinburgh, working on modelling sequential data, robot navigation, and climate research.

Jonathan collaborates with his team on cutting-edge machine learning problems, running experiments, discussing new ideas on the whiteboard, or presenting his latest work.

"At DeepMind, I can express my full technical creativity and work with the most inspiring researchers in my field.”

Team profile

Edward Lockhart
Research Engineer

Edward worked in quantitative finance for twenty years, developing skills in mathematics, statistics, and software engineering, which he applies to cutting-edge AI research.

Edward helps organise the research team and contributes to research efforts. He runs the Research Engineering Intern programme and develops agents that learn to collaborate.

"DeepMind is a great place to learn from world-leading experts who are eager to share their knowledge."