Prof. Sotirios Tsaftaris (University of Edinburgh, UK)
Title: Multimodal deep learning in biomedical image analysis
Abstract: Nowadays images are typically accompanied by additional information (e.g. the clinical history of the patient). At the same time, for example, magnetic resonance imaging exams typically contain more than one image modality: they show the same anatomy under different acquisition strategies revealing various pathophysiological information. The detection of disease, segmentation of anatomy and other classical analysis tasks, can benefit from a multimodal view to analysis that leverages shared information across the sources yet preserves unique (critical for diagnosis) information. It is without surprise that radiologists analyse data in this fashion, reviewing the exam as a whole. Yet, when aiming to automate analysis tasks, we still treat different image modalities in isolation and tend to ignore additional (non-image) information. In this talk, I will present recent work in learning with deep neural networks, latent embeddings suitable for multimodal processing, and highlight opportunities and challenges in this area.
Biography: Prof. Sotirios A. Tsaftaris, obtained his PhD and MSc degrees in Electrical Engineering and Computer Science (EECS) from Northwestern University, USA in 2006 and 2003 respectively. He obtained his Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece. Currently, he is a Chancellor’s Fellow (Senior Lecturer, US equivalent Associate Professor) in the School of Engineering at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute.
From 2006 to 2011, he was a research assistant professor with the Departments of EECS and Radiology, Northwestern University (USA). From 2011-2015, he was with IMT Institute for Advanced Studies, Lucca (Italy) serving as Director of the Pattern Recognition and Image Analysis Unit.
He is an Associate Editor for the IEEE Journal of Biomedical and Health Informatics and for Digital Signal Processing – Journal (Elsevier). He was Doctoral Symposium Chair for IEEE ICIP 2018 (Athens). He has served as area chair for IEEE ICME 2018 (San Diego), ICCV 2017 (Venice), MMSP 2016 (Montreal), and VCIP 2015 (Singapore). He has also co-organized workshops for ICCV (2017), ECCV (2014), BMVC (2015), and MICCAI (2016, 2017). He has also served as guest editor (IEEE Transactions on Medical Imaging; Digital Signal Processing – Software X; Machine Vision and Applications).
He has the received best paper award (STACOM 2017), twice the Magna Cum Laude Award (International Society for Magnetic Resonance in Medicine, ISMRM, in 2012 and 2014), and was a finalist for the Early Career Award (Society for Cardiovascular Magnetic Resonance, SCMR, in 2011).
He has authored more than 100 journal and conference papers particularly in interdisciplinary ﬁelds and his work is (or has been) supported by the National Institutes of Health (USA), EPSRC & BBSRC (UK), the European Union, the Italian Government, and several non-profits and industrial partners.
His research interests are in machine learning, image analysis (medical image computing), image processing, and distributed computing.
Prof. Tsaftaris is a Murphy, Onassis, and Marie Curie Fellow. He is also member of IEEE, ISMRM, SCMR, and IAPR.
Prof. Marios Polycarpou (University of Cyprus, KIOS Research Center)
Title: From Machine Learning to Machine Diagnostics
Abstract: During the last few years, there have has been remarkable progress in utilizing machine learning methods in several applications that benefit from deriving useful patterns among large volumes of data. These advances have attracted significant attention from industry due to the prospective of reducing the cost of predicting future events and making intelligent decisions based on data from past experiences. In this context, a key area that can benefit greatly from the use of machine learning is the task of detecting and diagnosing abnormal behavior in dynamical systems, especially in safety-critical, large-scale applications. The goal of this presentation is to provide insight into the problem of detecting, isolating and self-correcting abnormal or faulty behavior in large-scale dynamical systems, to present some design methodologies based on machine learning and to show some illustrative examples. The ultimate goal is to develop the foundation of the concept of machine diagnostics, which would empower smart software algorithms to continuously monitor the health of dynamical systems during the lifetime of their operation.
Biography: Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He received the B.A degree in Computer Science and the B.Sc. in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and cooperative control systems, computational intelligence, fault diagnosis and distributed agents. Dr. Polycarpou has published more than 300 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.
Prof. Polycarpou is a Fellow of IEEE and IFAC. He is the recipient of the 2016 IEEE Neural Networks Pioneer Award. He received with his co-authors the 2014 Best Paper Award for the journal Building and Environment (Elsevier). Prof. Polycarpou served as the President of the IEEE Computational Intelligence Society (2012-2013), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). He is currently the President of the European Control Association (EUCA). Prof. Polycarpou has participated in more than 60 research projects/grants, funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant.
Prof. Robert Kozma (University of Massachusetts Amherst, USA)
Title: Cognitive Phase Transitions in the Cerebral Cortex
Biography: Dr. Kozma holds a Ph.D. in Physics (Delft, The Netherlands, 1992), two M.Sc. degrees (Mathematics, Budapest, Hungary, 1988; Power Engineering, Moscow, Russia, 1982). He is Professor of Mathematical Sciences and Director of the Center of Large-Scale Integration and Optimization Networks (CLION), the University of Memphis, TN, USA. He is Visiting Professor at College of Information and Computer Sciences, University of Massachusetts Amherst, where he is Director of the Biologically-Inspired Neural and Dynamical Systems (BINDS) Lab, and leads the DARPA Program on Superior Artificial Intelligence.
Previous affiliations include joint appointment with the Division of Neurobiology and the EECS at UC Berkeley (1998-2000), and visiting positions at NASA/JPL, Sarnoff Co., Princeton, NJ; Lawrence Berkeley Laboratory (LBL); and AFRL WPAFB, Dayton, OH. He has been Associate Professor at Tohoku University, Sendai, Japan, Lecturer at Otago University, Dunedin, New Zealand, and Research Fellow at the Hungarian Academy of Sciences, Budapest, Hungary. His research is focused on computational neurodynamics, large-scale brain networks, and applying biologically motivated and cognitive principles for the development of intelligent systems. Dr. Kozma has published 8 books, 350+ papers, and 2 patents. His most recent book has been co-authored by Walter J. Freeman III on “Cognitive Phase Transitions in the Cerebral Cortex – Enhancing the Neuron Doctrine by Modeling Neural Fields,” Springer, Germany (2016). Dr. Kozma’s research has been supported by NSF, NASA, JPL, AFRL, AFOSR, DARPA, FedEx, and by other agencies.
Dr. Kozma is Fellow of IEEE and Fellow of the International Neural Network Society (INNS). He is President (2017-2018) of INNS, and serves on the Governing Board of IEEE Systems, Man, and Cybernetics Society (2016-2018). He has served on the AdCom of the IEEE Computational Intelligence Society (2009-2012) and the Board of Governors of the International Neural Network Society (2007-2012). He has been General Chair of IJCNN2009, Atlanta, USA. He is Associate Editor of Neural Networks, Neurocomputing, IEEE Transactions of Cybernetics, Cognitive Systems Research, and Cognitive Neurodynamics. Dr. Kozma is the recipient of “Gabor Award” of the International Neural Network Society (2011); the “Alumni Association Distinguished Research Achievement Award” (2010); he has been a “National Research Council (NRC) Senior Fellow” (2006-2008).
Remaining Keynote speakers to be announced.