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 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-creator of Codalab open source platform for challenges organization. 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 Chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 250 research papers and participated in the organization of scientific events, including CCIA04, ICCV11, CCIA14, AMDO16, FG17, NIPS17, NIPS18, FG19, and workshops at ICCV, ICMI, ECCV, CVPR, ICPR, NIPS. He has been guest editor at JMLR, TPAMI, IJCV, TAC, PR, MVA, JIVP, Expert Systems, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, AVSS17, FG17, ICCV17, WACV18, FG18, BMVC18, NIPS18, FG19 and competition and demo chair at FG17, NIPS17, NIPS18, ECMLPKDD19 and FG19. His research interests include, statistical pattern recognition, affective computing, and human pose recovery and behavior understanding, including multi-modal data analysis, with special interest in characterizing people: personality and psychological profile computing.
Hugo Jair Escalante
Hugo Jair Escalante is researcher scientist at Instituto Nacional de Astrofisica, Optica y Electronica, INAOE, Mexico. Previously, he was assistant professor at the Graduate Program on Systems Engineering at UANL. 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 secretary and member of the board of directors of ChaLearn, The Challenges in Machine Learning Organization, a non-profit organism dedicated to organizing challenges, since 2011. Also, he is member of the board of the CONACYT Network on Applied Computational Intelligence, regular member of AMEXCOMP and member of the National System of Researchers (SNI). Since 2017, he is editor of the Springer Series on Challenges in Machine Learning, a new book series focused on academic competitions within machine learning and related fields. He has been involved in the organization of several challenges in computer vision and machine learning, collocated with top venues in machine learning and computer vision, see http://chalearnlap.cvc.uab.es/. He has served as co-editor of special issues in IJCV, IEEE TPAMI, and IEEE Transactions on Affective Computing. He has served as area chair for NIPS 2016 and NIPS 2017, and has been member of the program committee of venues like CVPR, ICPR, ICCV, ECCV, ICML, NIPS, IJCNN. His research interests are on machine learning, evolutionary computing and its applications on language and vision.
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.
Universitat de Barcelona and Computer Vision Center, Spain
Meysam Madadi received his Bachelor degree in Software Engineering at BuAli Sina university of Hamedan and M.S. degree in Computer Vision and Artificial Intelligence at Universitat Autònoma de Barcelona (UAB) in 2007 and 2013, respectively. He has started his research activities by focusing on information retrieval and data mining since his bachelor project, continuing in master specifically on computer vision and image processing. He gave a special attention to pose recovery and human behavior analysis from his master thesis in title of Extraction of body soft-biometry from 3D videos using Kinect. He is interested in generating and developing new algorithms in these topics applying the knowledge in computer vision and retrieval systems besides machine learning, algorithms design in artificial intelligence, statistics, and linear algebra, among others.
Universitat de Barcelona, Universitat Politècnica de Catalunya and Universitat Rovira i Virgili, Barcelona, Spain
obtained his degree in Computer Science at Universitat de Barcelona, Spain in 2016. He is currently working towards a master's degree in Artificial Intelligence at Universitat de Barcelona, Universitat Politècnica de Catalunya and Universitat Rovira i Virgili. His research interests are Computer Vision, Generative Models and Representation Learning.
Fraunhofer Institute FOKUS, Berlin, Germany
is a researcher at Fraunhofer Institute FOKUS in Berlin, Germany. He obtained his Ph.D. in Mechanical Engineering from Northwestern University, USA. He has been the recipient of awards such as the Marie Curie Excellence Team grant from the European Commission. He is currently the coordinator of H2020 Big Data (ICT-16b) grant 'See.4C' which consists of the preparation of a large-scale challenge in spatio-temporal forecasting for the European Commission. He serves on editorial board of such journals as Journal of Artificial General Intelligence, has co-organized Symposia and Workshops at NIPS, has co-edited a volume of JMLR Workshops and Conference Proceedings on Time-Series Causality and has recently served as a reviewer for NIPS and KDD. His current reasearch interests include spatio-temporal forecasting for energy and smart city systems, as well as automated biosignal processing and diagnostics.