Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. He has secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos.

Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. Prior to joining Facebook in August 2014, he worked at MuseAmi, where he built deep learning models for music and vision targeted at mobile devices. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.

Jan Koutnik received his Ph.D. in computer science from the Czech Technical University in Prague in 2008, where he also served as assistant professor in computer science. In 2009 he joined The Swiss AI Lab IDSIA as machine learning researcher. His research was focused on artificial neural networks, recurrent neural networks, evolutionary algorithms and deep-learning applied to reinforcement learning, control problems, image classification, handwriting and speech recognition. In 2014 he co-founded NNAISENSE, which he joined full time in 2016 as director of intelligent automation. He was the team leader at learning to park demonstration, presented, together with Audi AG, at NIPS'2016 and learning to run competition, that NNAISENSE won at NIPS'2017.

Moustapha Cisse is a research scientist at Google. He is head of the Google AI center in Accra, Ghana where he leads research efforts in foundational machine learning and its applications to solving complex societal challenges. Moustapha is also a professor of machine learning at the African Institute of Mathematical Sciences and the founder and director of the African Masters of Machine Intelligence. He was previously a research scientist at Facebook AI Research. Before, he studied Mathematics and Physics at University Gaston Berger in Senegal and did his PhD at University Pierre and Marie Curie in France.

Klaus Greff is a Ph.D. student at IDSIA under the supervision of Jürgen Schmidhuber. He did his masters degree in computer science at the University of Kaiserslautern.

Armand is a research manager at Facebook AI Research . Prior to this position, he was a postdoctoral fellow at Stanford University working with Fei-Fei Li and Daphne Koller. He did his PhD in INRIA and Ecole Normale Superieure, under the supervision of Francis Bach and Jean Ponce. He did his undergrad at Ecole Polytechnique. His subjects of interest are machine learning, computer vision and natural language processing.

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006. He is the head of the Machine Learning group at the Idiap Research Institute, Switzerland, since 2007, and adjunct faculty in the School of Engineering of the École Polytechnique Fédérale de Lausanne since 2011, where he teaches machine learning. He has published more than 80 papers in peer-reviewed international conferences and journals. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) since 2012, served as Area Chair for NIPS (2012, 2014, 2016, 2017, 2018) and ICCV (2012) and in the program committee of many top-tier international conferences in machine learning and computer vision. He is member of the Electrical Engineering Doctoral Program Committee at EPFL, and was or is expert for multiple funding agencies (Swiss National Science Foundation, European Research Council, Austrian Science Fund, Netherlands Organization for Scientific Research, French National Research Agency, Research Council of the Academy of Finland, US National Science Foundation). His main research interest is machine learning, with a particular focus on computational aspects and small sample learning, and applications in computer vision.

Paolo Favaro received the Laurea degree (B.Sc.+M.Sc.) from Università di Padova, Italy in 1999, and the M.Sc. and Ph.D. degree in electrical engineering from Washington University in St. Louis in 2003 and 2004 respectively. He was a postdoctoral researcher in the computer science department of the University of California, Los Angeles and subsequently in Cambridge University, UK. Between 2004 and 2006 he worked in medical imaging at Siemens Corporate Research, Princeton, USA. From 2006 to 2011 he was Lecturer and then Reader at Heriot-Watt University and Honorary Fellow at the University of Edinburgh, UK. In 2012 he became full professor at Universität Bern, Switzerland. His research interests are in computer vision, computational photography, machine learning, signal and image processing, estimation theory, inverse problems and variational techniques. He is also a member of the IEEE Society.