Organizers


Pat Jangyodsuk

University of Texas at Arlington, Arlington, United States of America

pat.jangyodsuk@mavs.uta.edu

A CS Ph.D candidate at University of Texas at Arlington. Working on machine learning / computer vision problems such as object recognition, time series classification and indexing in large scale database. 3 years experience in industrial setting as a software engineer. 10+ years coding experience. Expertise in many programming languages and web technology including Java, Python, C# C++, SQL, J2EE, JSP, JSON, Ajax and .NET Framework. Skilled in fundamental computer science theory especially algorithm such as graph theory, sorting, complexity, recursion and dynamic programming. Self-motivated, willing to learn, passionate, highly responsible. Solid English communication skill.

Vassilis Athitsos

University of Texas at Arlington, Arlington, United States of America

athitsos@uta.edu

Vassilis Athitsos received the BS degree in mathematics from the University of Chicago in 1995, the MS degree in computer science from the University of Chicago in 1997, and the PhD degree in computer science from Boston University in 2006. In 2005-2006 he worked as a researcher at Siemens Corporate Research, developing methods for database-guided medical image analysis. In 2006-2007 he was a postdoctoral research associate at the Computer Science department at Boston University. In August 2007 he joined the Computer Science and Engineering department at the University of Texas at Arlington, where he currently serves as associate professor. His research interests include computer vision, machine learning, and data mining. His recent work has focused on gesture and sign language recognition, detection and tracking of humans using computer vision, efficient similarity-based retrieval in multimedia databases, shape modeling and detection, and medical image analysis. His research has been supported by the National Science Foundation, including an NSF CAREER award.

Hugo Jair Escalante

INAOE, México

hugojair@inaoep.mx

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.

Isabelle Guyon

University Paris-Saclay, France and ChaLearn USA

isabelle@clopinet.com

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.

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