Upcoming Webinar: 15 March 2018, Dr. Sergios Theodoridis and Dr. Sotirios Chatzis

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News and Resources for Members of the IEEE Signal Processing Society

Upcoming Webinar: 15 March 2018, Dr. Sergios Theodoridis and Dr. Sotirios Chatzis

Webinar Details: 15 March 2018 from 9:00am - 10:00am (EDT) 
Attendee Registration Link: Register for Webinar, by Dr. Sergios Theodoridis and Dr. Sotirios Chatzis
Topic: A Tour into Deep Learning: From Its Origins to Cutting Edge Research and Open Challenges

The IEEE Signal Processing Society is very excited to offer the next webinar in our new series featuring “technical and technological innovations and their applications in signal processing” from top researchers and thought leaders in academia and industry. This webinar, "A Tour into Deep Learning: From Its Origins to Cutting Edge Research and Open Challenges," being presented by Dr. Sergios Theodoridis, VP-Publications, IEEE Signal Processing Society in collaborations with Dr. Sotirios Chatzis), will provide a tour into neural networks and deep architectures, beginning with a historical retrospective from the early days of the perceptron through the recent trends of convolutional neural networks (CNNs) and the recurrent neural networks (RNNs).

Dr. Theodoridis will discuss the reasons of the comeback of neural networks, after the reigning period of the kernel machines, and present the key ideas that contributed to their current domination in the machine learning territory. The major developments that led to overcome some of their early drawbacks will be outlined and discussed, such as the ReLu and the Dropout technique.

Please join Dr. Theodoridis on March 15, 2018 from 9 am to 10 am (EDT) as he will give examples of some notable applications and case studies. He will also discuss some of their limitations and provide a short overview of the open problems that are currently under investigation. At the conclusion, Dr. Theodoridis will offer his insight and provide some future predictions...at his own risk!

If you have any questions, please contact Bill Colacchio, Senior Manager Publications, Education Strategy and Services, IEEE Signal Processing Society at w.colacchio@ieee.org.


About the Presenters:

Sergios Theodoridis

Sergios Theodoridis is currently Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens and he is the holder of a part time Chair at the Chinese University of Hong Kong. His research interests lie in the areas of Adaptive Algorithms, Distributed and Sparsity-Aware Learning, Machine Learning and Pattern Recognition, Signal Processing and Learning for Bio-Medical Applications and Audio Processing and Retrieval.

He is the author of the book “Machine Learning: A Bayesian and Optimization Perspective,” Academic Press, 2015, the co-author of the best-selling book “Pattern Recognition,” Academic Press, 4th ed., 2009, the co-author of the book “Introduction to Pattern Recognition: A MATLAB Approach,” Academic Press, 2010, the co-editor of the book “Efficient Algorithms for Signal Processing and System Identification,” Prentice Hall, 1993, and the co-author of three books in Greek, two of them for the Greek Open University.

He is the co-author of seven papers that have received Best Paper Awards including the 2014 IEEE Signal Processing Magazine Best Paper award and the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award. He is the recipient of the 2017 EURASIP Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Education Award and the 2014 EURASIP Meritorious Service Award. He has served as a Distinguished Lecturer for the IEEE Signal Processing as well as the Circuits and Systems Societies. He was Otto Monstead Guest Professor, Technical University of Denmark, 2012, and holder of the Excellence Chair, Dept. of Signal Processing and Communications, University Carlos III, Madrid, Spain, 2011. He has served as Editor-in-Chief for the IEEE Transactions on Signal Processing. He is Editor-in-Chief for the Signal Processing Book Series, Academic Press and co-Editor in Chief for the E-Reference Signal Processing, Elsevier.

He has served as President of the European Association for Signal Processing (EURASIP), as a member of the Board of Governors for the IEEE Circuits and Systems (CAS) Society, as a member of the Board of Governors (Member-at-Large) of the IEEE SP Society and as a Chair of the Signal Processing Theory and Methods (SPTM) technical committee of IEEE SPS. He currently serves as Vice President IEEE Signal Processing Society. He is Fellow of IET, a Corresponding Fellow of the Royal Society of Edinburgh (RSE), a Fellow of EURASIP and a Fellow of IEEE.


Sotirios ChatzisSotirios Chatzis is currently an Assistant Professor with the Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus.

He has been key author to more than 70 papers in the most prestigious journals and conferences of the research field. His current research interests include machine learning theory and methodologies, specifically deep latent variable models, approximate variational inference, neural attention models with differentiable memory modules, and Bayesian nonparametrics. His Ph.D. research was supported by the Bodossaki Foundation, Greece, and the Greek Ministry for Economic Development.

Dr. Chatzis was a recipient of the Dean’s scholarship for Ph.D. studies, being the best performing Ph.D. student of the class. He was a Post-Doctoral Fellow with the University of Miami, Coral Gables, FL, USA, from 2008 to 2010. He was a Post-Doctoral Researcher with the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K., from 2010 to 2012. He has coordinated or participated as primary investigator in five EU-funded research projects, dealing with the application of machine learning in a wide range of application domains, including web privacy and security, recommendation systems for the promotion of European cultural heritage, learning from streaming data, and computational finance.


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