Dr. Fernand S. Cohen, Director of the Imaging and Computer Vision Center, Drexel University, Philadelphia, USA.
Title: The Need for ‘Cultural’ Machine Translation – Challenges and Solutions.
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Bio: Fernand S. Cohen (IEEE SM 96) received his B. Sc. degree in Physics from the American University in 1978, and M. Sc. and Ph.D. degrees in Electrical Engineering from Brown University, Providence, RI, in 1980 and 1983, respectively. He joined the Department of Electrical Engineering at the University of Rhode Island in 1983 as an Assistant Professor. In 1984 he joined the Robotics Research Center, University of Rhode Island and was responsible for the Vision Research in the center from 1986-1987. In 1985 he was the recipient of a Research Excellence Award from the College of Engineering, University of Rhode Island. He was in 1986 invited by the French government (mission scientifique) to tour research laboratories and universities. In 1987 he joined the Department of Electrical and Computer Engineering at Drexel University as a named Chair Associate Professor (George Beggs). He is currently Professor of Electrical and Computer Engineering and is affiliated with the School of Biomedical Engineering, Science and Health Systems, and serves as Director of the Imaging and Computer Vision Center (ICVC). In the summer 1994 he was invited as a visiting Professor by the National Institute of Research in Information and Automation (INRIA) in Sophia Antipolis, France. He was awarded the Tom Moore Teaching Award, ECE Department, Drexel University, in May 2003. He was also the recipient of a CNRS (Centre National de Recherche Scientique) fellowship in the summer of 2005. He has worked in the area of computer vision and sensor networks, as well as in the area of early cancer detection using ultrasound and optical probes and has published extensively in these areas over the last three decades. He published over 130 journal and reviewed conference publications and has graduated 15 Ph. D. students, and has had over 10 million dollars funding from NIH, NSF, and NSA. He has numerous papers with over 200 citations each. His most recent projects are: computational archaeology (NFS) and person recognition and tracking in crowded scenes (NSA). He was keynote speakers at many conferences more recently in Bangkok, Barcelona, and Morocco. His research interests include pattern recognition, computer vision, medical image processing, computational methods, sensor networks, and applied stochastic processes.
Dr. Mohamed Nadif, University Paris Descartes, France.
Title: Co-clustering for Data Science.
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Bio: Mohamed Nadif is full Professor of data analysis, machine learning and data mining at University Paris Descartes. He is the head of "Machine Learning for Data Science" (MLDS) team at LIPADE since 2006. Since twenty years, he supervised many thesis on statistics and machine learning on theoretical and applied subjects with industries. He has more than 140 publications and is the one of the two authors of the book (Wiley Ed) entitled «Co-clustering: Models, algorithms and applications», 2013. He teaches machine learning techniques and multivariate analysis in master "Machine Learning for Data Science" that he has created and continue to supervise since 2009.
He is a regular member of the program committee in international conferences such as IJCAI, ECML and IDA, reviewer in prestigious journals such as IEEE Pami, IEEE TNNLS, and Pattern recognition, associate editor of the “Advances in Analysis and Classification” journal. His current researches interests include cluster analysis, co-clustering, data analysis, visualization, factorization latent block models, text-mining and recommender systems. He was Invited by Amirkabir University (Teheran-IRAN) and China University of Petroleum (Huangdao-China), and several french and international conferences.
Since 2012, he is the president of the French speaking Society of Classification (SFC) which is a member of IFCS (International Federation of Classification Societies). He has been awarded many grants such as ANR ClasSel- 2008-2012 on co-clustering and model selection and EDAGWAS 2013-2015 devoted to develop a set user-friendly and fast data analysis tools to explore both association between genome-wide SNP data, disease and textual relationships among genes. He is a contributor of the “Coclust” software providing diagonal and non-diagonal co-clustering algorithms developed in the MLDS team and exclusively dedicated to high-dimensional sparse data.
