Ralf is Director of Machine Learning at Amazon and Managing Director of the Amazon Development Center Germany. His team works on problems scalable and resource-aware machine learning, probabilistic learning algorithms (including forecasting), linking structured content, and computer vision. In 2011, he worked at Facebook leading the Unified Ranking and Allocation team. From 2000 – 2011, he worked at Microsoft Research and was co-leading the Applied Games and Online Services and Advertising group which engaged in research at the intersection of machine learning and computer games. Ralf was Research Fellow of the Darwin College Cambridge from 2000 – 2003. He has a diploma degree in Computer Science (1997) and a PhD in Statistics (2000). Ralf’s research interests include Bayesian inference and decision making, reinforcement learning, computer games, kernel methods, and statistical learning theory. He is one of the inventors of the Drivatars system in the Forza Motorsport series as well as the TrueSkill ranking and matchmaking system in Xbox 360 Live. He also co-invented the adPredictor click-prediction technology.
Mustafa Suleyman is co-founder and Head of Applied AI at DeepMind, where he is responsible for the application of DeepMind’s technology to real-world problems, as part of DeepMind’s commitment to use intelligence to make the world a better place. In February 2016 he launched DeepMind Health, which builds clinician-led technology in the NHS. Mustafa was Chief Product Officer before DeepMind was bought in 2014 by Google in their largest European acquisition to date. At 19, Mustafa dropped out of Oxford University to help set up a telephone counselling service, building it to become one of the largest mental health support services of its kind in the UK, and then worked as policy officer for then Mayor of London, Ken Livingstone. He went on to help start Reos Partners, a consultancy with seven offices across four continents specializing in designing and facilitating large-scale multi-stakeholder ‘Change Labs’ aimed at navigating complex problems. As a skilled negotiator and facilitator Mustafa has worked across the world for a wide range of clients such as the UN, the Dutch Government, and WWF.
Greg Corrado is a senior scientist at Google Research, and a co-founder of the Google Brain Team. He works at the nexus of artificial intelligence, computational neuroscience, and scalable machine learning, and has published in fields ranging from behavioral economics, to particle physics, to deep learning. In his time at Google he has worked to put AI directly into the hands of users via products like RankBrain and SmartReply, and into the hands of developers via opensource software releases like TensorFlow and word2vec. He currently leads several research efforts in advanced applications of machine learning, ranging from natural human communication to expanded healthcare availability. Before coming to Google, he worked at IBM Research on neuromorphic silicon devices and large scale neural simulations. He did his graduate studies in both Neuroscience and Computer Science at Stanford University, and his undergraduate work in Physics at Princeton University.
Yann is the Director of AI Research at Facebook since December 2013, and Silver Professor at New York University on a part-time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Science.
Yann received the EE Diploma from Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique (ESIEE Paris), and a PhD in CS from Université Pierre et Marie Curie (Paris). After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, New Jersey. Yann became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he was the founding director of the NYU Center for Data Science.
Yann is the co-director of the Neural Computation and Adaptive Perception Program of CIFAR, and co-lead of the Moore-Sloan Data Science Environments for NYU. He received the 2014 IEEE Neural Network Pioneer Award.
Francesca Rossi is a research scientist at the IBM T.J. Watson Research Centre and a professor of computer science at the University of Padova, Italy.
Francesca’s research interest focuses on artificial intelligence, specifically constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues surrounding the development and behavior of AI systems, in particular for decision support systems for group decision making. A prolific author, Francesca has published over 170 scientific articles in both journals and conference proceedings as well as co-authoring A Short Introduction to Preferences: Between AI and Social Choice. She has edited 17 volumes, including conference proceedings, collections of contributions, special issues of journals, and The Handbook of Constraint Programming.
Francesca is both a fellow of the European Association for Artificial Intelligence (EurAI fellow) and also a 2015 fellow of the Radcliffe Institute for Advanced Study at Harvard University. A prominent figure in the Association for the Advancement of Artificial Intelligence (AAAI), at which she is a fellow, she has formerly served as an executive councilor of AAAI and currently co-chairs the association’s committee on AI and ethics. Francesca is an active voice in the AI community, serving as Associate Editor in Chief of the Journal of Artificial Intelligence Research (JAIR) and as a member of the editorial boards of Constraints, Artificial Intelligence, Annals of Mathematics and Artificial Intelligence (AMAI), and Knowledge and Information Systems (KAIS). She is also a member of the scientific advisory board of the Future of Life Institute, sits on the executive committee of the Institute of Electrical and Electronics Engineers (IEEE)’s global initiative on ethical considerations on the development of autonomous and intelligent systems, and belongs to the World Economic Forum Council on AI and robotics.
A recognized authority on the future of AI and AI ethics, Francesca has been interviewed widely by publications including the Wall Street Journal, the Washington Post, Motherboard, Science, The Economist, CNBC, Eurovision, Corriere della Sera, and Repubblica, and has also delivered three TEDx talks on these topics.
Eric Horvitz is technical fellow at Microsoft, where he serves as director of the Microsoft Research lab at Redmond. His research contributions span theoretical and practical challenges with computing systems that learn from data and that can perceive, reason, and decide. His efforts have helped to bring multiple systems and services into the world, including innovations in transportation, healthcare, aerospace, ecommerce, online services, and operating systems. He has been elected fellow of the National Academy of Engineering (NAE), the Association for the Advancement of AI (AAAI), the American Association for the Advancement of Science (AAAS), and the American Academy of Arts and Sciences. He received the Feigenbaum Prize, the ACM-AAAI Allen Newell Award, and the ACM ICMI Sustained Achievement Award for foundational research contributions in AI. He was inducted into the CHI Academy for advances in human-computer collaboration. He has served as president of AAAI, chair of the AAAS Section on Computing, and on advisory committees for the National Institutes of Health, the National Science Foundation, the Computer Science and Telecommunications Board (CSTB), DARPA, and the President’s Council of Advisors on Science and Technology. He received his PhD and MD degrees from Stanford University. More information can be found at research.microsoft.com/~horvitz.