Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has …
Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.
Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book Mathematics for Machine Learning published by Cambridge University Press (2020).
Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.
..* CEO & Founder of machinelearning.fr
..* MSc. Machine Learning, University College London, 2013, UK ..* Computer Science Engineer's degree, Université de Technologie de Compiègne (UTC), 1983, France
..* Co-organiser of the Paris Machine Learning Applications Meetup (+8000 members) ..* Organiser of the Centre-Loire Valley Machine Learning Meetup ..* Organiser of the PyData Loire Valley
I hold a 4-year Bachelor of Science in Informatics and 2-year Master of Science in Statistical Science both from Athens University of Economics and Business and now I am a second year PhD student at MRC-BSU, University of Cambridge. During my undergraduate studies I was delighted to explore the essentials of information theory and theoretical computer science (complexity, computability, asymptotic theory, algorithmic game theory). Later, as postgraduate student I focused on Bayesian theory and applications, under the insightful supervision of Professor Petros Dellaportas. My fields of interest lie in Bayesian high-dimensional problems, mixture models and probabilistic machine learning.
Pascale Fung (馮雁) (born 1966 in Shanghai, China) is a professor in the Department of Electronic & Computer Engineering and the Department of Computer Science & Engineering at the Hong Kong University of Science & Technology(HKUST). She is the director of the newly established, multidisciplinary Centre for AI Research (CAiRE) at HKUST. She is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her “contributions to human-machine interactions” and an elected Fellow of the International Speech Communication Association for “fundamental contributions to the interdisciplinary area of spoken language human-machine interactions”.
Freddie is a part-time Senior Research Fellow, and Theme Lead of ML for Earth Observation and Remote Sensing, in the Oxford Applied and Theoretical Machine Learning lab (led by Yarin Gal) of Oxford University. He's also an ML & Project Lead at NASA's Frontier Development Lab (FDL), and the (part-time) ML Lead of Trillium Technologies , the R&D production company behind FDL.
Since FDL US 2020, Freddie has been a ML & Project Lead for project Waters Of The United States (WOTUS), in partnership with the USGS, Planet, Maxar, Google Cloud and NVIDIA, towards the ultimate vision for mapping all flowing water on Earth , at near real-time, by fusing LiDAR sensors and daily very high resolution (VHR) satellite imagery.
He started his journey with FDL 2019 as a mentor , helping teams super-resolve solar magnetograms and predict GPS disruptions induced by solar weather .
Until April 2020, he was an Applied Research Scientist in the AI for Good lab (led by Julien Cornebise) of Element AI in London, focusing on applications of ML and statistics that enable NGOs and nonprofits.
During this work, he led the Multi-Frame Super-Resolution research collaboration with Mila Montréal , which was awarded by ESA …