Invited Speakers.
​David Ha (Google Brain). David is a Research Scientist at Google Brain. His research interests include representation learning, artificial creativity, and evolutionary computing. Prior to joining Google, He worked at Goldman Sachs as a Managing Director, where he ran the fixed-income trading business in Japan. He obtained undergraduate and graduate degrees in Engineering Science and Applied Math from the University of Toronto.

Twitter: @hardmaru
Raia Hadsell (DeepMind). Raia Hadsell leads Robotics research at DeepMind. Her early research was in the use of Siamese networks for learnt embeddings, an approach which is now commonly used for representation learning. After completing a PhD which featured a self-supervised deep learning vision system for a mobile robot, her research continued at Carnegie Mellon’s Robotics Institute and SRI International, and in early 2014 she joined DeepMind in London to study artificial general intelligence. Dr. Hadsell’s current research focuses on the challenge of continual learning for AI agents and robots, and she has proposed neural approaches such as policy distillation, progressive nets, and elastic weight consolidation to solve the problem of catastrophic forgetting.

​Twitter: @RaiaHadsell
Vladlen Koltun (Intel) is a Senior Principal Researcher and the director of the Intelligent Systems Lab at Intel. His lab conducts high-impact basic research on intelligent systems, with emphasis on computer vision, robotics, and machine learning. He has mentored more than 50 PhD students, postdocs, research scientists, and PhD student interns, many of whom are now successful research leaders.
Katja Hofmann (Microsoft Research). Dr. Katja Hofmann is a Principal Research Manager at the Game Intelligence group at Microsoft Research Cambridge, UK. There she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems.
 
Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the ILPS group at the University of Amsterdam. She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.

Twitter: @​katjahofmann
Karl Cobbe (OpenAI). Karl Cobbe is currently a research scientist at OpenAI. He received his BS in computer science with distinction from Stanford University. He first joined OpenAI as a research fellow in 2018, working under the mentorship of John Schulman. During this time, he helped create CoinRun, an environment for benchmarking generalization in deep reinforcement learning. Karl is particularly interested in leveraging procedural generation to create diverse training environments, to better investigate the limitations of state-of-the-art algorithms and the factors that lead to overfitting.

Twitter: @karlcobbe
​Wojciech Zaremba (OpenAI) is a co-founder of OpenAI, where he leads the robotics team. The focus of the team is developing general purpose robots. Most recently, the group has shown how to train a humanoid robotic hand to manipulate objects such as a block or pyramid. Wojciech co-discovered that neural networks are susceptible to imperceptible perturbations, which defined the new field of adversarial attacks on neural networks. Wojciech is also a co-developer of OpenAI Gym, which has become a standard tool in reinforcement learning.  Wojciech’s research contributions have involved the creation of inception score, domain randomization, and hindsight experience replay. Prior to OpenAI, he spent time at Facebook AI Research and Google Brain. He finished a Ph.D. at New York University in 2.5 years. 

Twitter: @​woj_zaremba
Prof Gianni De Fabritiis is an Icrea research professor in Barcelona and associate professor at the University
Pompeu Fabra. In 2006, he founded Acellera UK with a mission to accelerate the transition towards
computerized drug discovery. He graduated in applied mathematics (1997, University of Bologna) with
laude. He went on to work at the Italian CINECA supercomputing centre (1998-1999), then completed a
PhD (2002) from Queen Mary University of London. After one year working in a start-up in business
intelligence, he took a position at University College London (2003-2006) and later won a tenure track
position at University Pompeu Fabra. He is currently ICREA research professor and head of the
computational science laboratory at the University Pompeu Fabra. The group research interests are rooted
in application of computation to solve real world problems where we view intelligence as a form of
computation. Specifically, we develop new methods that we apply to AI, drug design, protein folding, etc.
The research lines of the group are to develop machine learning models with the aim to attain, in specific
environments, intelligent, useful behavior using reinforcement learning and deep learning. One such
environment is biomedicine where we use computation, as physics-based simulations and machine learning
to provide novel, innovative approaches.

Twitter: @gdefabritiis