Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (LISA), CIFAR Fellow in the Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms, and he also holds the NSERC-Ubisoft industrial chair. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning (IFT6266) and supervises a large group of graduate students and post-docs. His research is widely cited (over 16000 citations found by Google Scholar in early 2014, with an H-index of 55). Yoshua Bengio is currently action editor for the Journal of Machine Learning Research, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks and Program Chair for NIPS'2008 and General Chair for NIPS'2009 . Since 1999, he has been co-organizing the Learning Workshop with Yann Le Cun, with whom he has also created the International Conference on Representation Learning (ICLR). He has also organized or co-organized numerous other events, such as the ICML'2012 Representation Learning Workshop, the NIPS'2011 Deep Learning and Unsupervised Feature Learning Workshop, the NIPS'2010 Deep Learning and Unsupervised Feature Learning Workshop, the ICML'2009 Workshop on Learning Feature Hierarchiesand the NIPS'2007 Deep Learning Workshop.
Xiaodong He is a Researcher in the Deep Learning Technology Center of Microsoft Research and an Affiliate Professor in the Department of Electrical Engineering at the University of Washington, Seattle, USA. His research interests include deep learning, information retrieval, spoken language understanding, machine translation, natural language processing, speech recognition, and cross-modality learning. He and his colleagues developed the MSR-NRC-SRI entry and the MSR entry which obtained No. 1 place in the 2008 NIST MT Evaluation and No. 1 place in the 2011 IWSLT Evaluation, all in Chinese-English translation, respectively. Recently, his research focus is on Deep Learning for Text/Vision/Semantic Processing. Relevant studies are summarized in his tutorials at ICASSP 2014 and CIKM 2014. He has held editorial positions on several IEEE Journals, and has served as area chair and organizing/program committee member for conferences in speech and language processing. He is a member of IEEE SLTC.
Randall C. O’Reilly received his Ph.D. in 1996 from Carnegie Mellon University. After a one-year postdoc at the Massachusetts Institute of Technology, he joined the faculty at the University of Colorado at Boulder in the Fall of 1997. Dr. O'Reilly's overall research goal is to understand the biological and computational bases of human cognition. This is a particularly challenging endeavor because of the complexity of both cognition and neurobiology. To make progress, some simplifying assumptions are required. Computational models, based on simplified biological neurons, play this role in his work. These models are capable of making links between important biological properties and cognitive phenomena, and doing so in a way that can provide explanations and insight that were not available in considerations of either neurobiology or cognition separately. O'Reilly has focused this approach on understanding the roles of the hippocampus, posterior neocortex, and frontal neocortex in learning and memory.
Rahul Sukthankar is a scientist at Google Research, an adjunct research professor in the Robotics Institute at Carnegie Mellon and courtesy faculty in EECS at the University of Central Florida. He was previously a senior principal researcher at Intel Labs (2003-2011), a senior researcher at HP/Compaq Labs (2000-2003) and research scientist at Just Research (1997-2000). Rahul received his Ph.D. in Robotics from Carnegie Mellon in 1997 and his B.S.E. in Computer Science from Princeton in 1991. His current research focuses on computer vision and machine learning, particularly in the areas of object recognition, video understanding and information retrieval.
Andrea Vedaldi is an Associate Professor in Engineering Science, Tutorial Fellow at New College, and member of the VGG group at the University of Oxford. His research focuses on computer vision methods to understand the content of images automatically, with applications to organising and searching vast image and video libraries and recognising faces and text in images and videos. He is also the leading author of the VLFeat computer vision library.
Julie Bernauer attended ENS Cachan from 2001 to 2004 where she received a degree in Physical Chemistry. She obtained her PhD from Université Paris-Sud in 2006 while performing research in the Yeast Structural Genomics group. Her thesis focused on the use of Voronoi models for modelling protein complexes. After a post-doctoral position at Stanford University with Pr. Michael Levitt, Nobel Prize in Chemistry 2013, she joined Inria, the French National Institute for Computer Science. While Senior Research Scientist at Inria, Adjunct Associate Professor of Computer Science at École Polytechnique and Visiting Research Scientist at SLAC, her work focused on computational methods for structural bioinformatics, specifically scoring functions for macromolecule docking using machine learning, and statistical potentials for molecular simulations. She was the first to successfully introduce machine learning for coarse-grained models in the CAPRI challenge. Julie Bernauer recently joined NVIDIA Corporation as Senior Solutions Architect for Machine Learning..
Xiaogang Wang received his Bachelor degree in Electrical Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China in 2001, M. Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2004, and PhD degree in Computer Science from Massachusetts Institute of Technology in 2009. He is an assistant professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He received the Outstanding Young Researcher in Automatic Human Behaviour Analysis Award in 2011, Hong Kong RGC Early Career Award in 2012, and Young Researcher Award of the Chinese University of Hong Kong. He is the associate editor of the Image and Visual Computing Journal. He was the area chair of ICCV 2011, ECCV 2014, ACCV 2014, and ICCV 2015. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition.