Computer Vision Center (UAB) and University of Barcelona, Barcelona, Spain
Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at Campus UAB. He is series editor of The Springer Series on Challenges in Machine Learning. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-founder of PhysicalTech and Care Respite companies. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, INNS, and vice-chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 150 research papers and participated in the organization of scientific events, including CCIA04, CCIA14, ICCV11, AMDO16, FG17, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16, CVPR17. He has been guest editor at JMLR, TPAMI, IJCV, TAC, PR, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, AVSS17, FG17, and ICCV17, and competition and demo chair at FG17 and NIPS17. His research interests include, between others, statistical pattern recognition, visual object recognition, affective computing, and HCI systems, with special interest in human pose recovery and behavior analysis from multi-modal data.
Hugo Jair Escalante
Hugo Jair Escalante is researcher scientist at Instituto Nacional de Astrofisica, Optica y Electronica, INAOE, Mexico. He holds a PhD in Computer Science, for which he received the best PhD thesis on Artificial Intelligence 2010 award (Mexican Society in Artificial Intelligence). He was granted the best paper award of the International Joint Conference on Neural Networks 2010 (IJCNN2010). He is a director of ChaLearn, a nonprofit dedicated to organizing challenges, since 2011. He has been involved in the organization of several challenges in computer vision and automatic machine learning. He is reviewer at JMLR, PAMI, and has served as coeditor of special issues in IJCV, PAMI, and IEEE Transactions on Affective Computing. He is serving as area chair for NIPS 2016, and has been member of the program committee of venues like CVPR, ICCV, ECCV, ICML, NIPS, IJCNN.
University Paris-Saclay, France and ChaLearn USA
Isabelle Guyon ( http://guyon.chalearn.org/ ) is chaired professor in “big data” at the Université ParisSaclay, specialized in statistical data analysis, pattern recognition and machine learning. She is one of the cofounders of the ChaLearn Looking at People (LAP) challenge series and she pioneered applications of the MIcrosoft Kinect to gesture recognition. Her areas of expertise include computer vision and and bioinformatics. Prior to joining ParisSaclay she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and coinvented with Bernhard Boser and Vladimir Vapnik Support Vector Machines (SVM), which became a textbook machine learning method. She worked on early applications of Convolutional Neural Networks (CNN) to handwriting recognition in the 1990’s. She is also the primary inventor of SVMRFE, a variable selection technique based on SVM. The SVMRFE paper has thousands of citations and is often used as a reference method against which new feature selection methods are benchmarked. She also authored a seminal paper on feature selection that received thousands of citations. She organized many challenges in Machine Learning since 2003 supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie, Paris, France. She is president of Chalearn, a nonprofit dedicated to organizing challenges, vicepresident of the Unipen foundation, adjunct professor at NewYork University, action editor of the Journal of Machine Learning Research, editor of the Challenges in Machine Learning book series of Microtome, and program chair of the upcoming NIPS 2016 conference.
Evelyne Viegas is the Director of Artificial Intelligence Outreach at Microsoft Research, based in Redmond, U.S.A. In her current role, Evelyne is building initiatives which focus on information seen as an enabler of innovation, working in partnership with universities and government agencies worldwide. In particular she is creating programs around computational intelligence research to drive open innovation and agile experimentation via cloud-based services; and projects to advance the state-of-the-art in data-driven research including knowledge representation, machine learning and reasoning under uncertainty at web scale. Prior to her present role, Evelyne has been working as a Technical Lead at Microsoft delivering Natural Language Processing components to projects for MSN, Office, and Windows. Before Microsoft, and after completing her Ph.D. in France, she worked as a Principal Investigator at the Computing Research Laboratory in New Mexico on an ontology-based Machine Translation project. Evelyne serves on international editorial, program and award committees.