President, IEEE Signal Processing Society
Ali H. Sayed
Ali H. Sayed is Dean of Engineering at EPFL, Switzerland. He has also served as distinguished professor and former chairman of electrical engineering at UCLA. An author of over 530 scholarly publications and six books, his research involves several areas including adaptation and learning, data and network sciences, and multi-agent systems. Dr. Sayed has received several awards including the 2015 Education Award from the IEEE Signal Processing Society, the 2014 Athanasios Papoulis Award from the European Association for Signal Processing, the 2013 Meritorious Service Award, and the 2012 Technical Achievement Award from the IEEE Signal Processing Society. He has also received the 2005 Terman Award from the American Society for Engineering Education, the 2003 Kuwait Prize, and the 1996 IEEE Donald G. Fink Prize. He served as Distinguished Lecturer for the IEEE Signal Processing Society in 2005 and as Editor-in-Chief of the IEEE TRANSACTIONS ON SIGNAL PROCESSING (2003–2005). His articles received several Best Paper Awards from the IEEE (2002, 2005, 2012, 2014). He is a Fellow of IEEE and the American Association for the Advancement of Science (AAAS). He is recognized as a Highly Cited Researcher by Thomson Reuters. He is also a member of the US National Academy of Engineering and serves as President of the IEEE Signal Processing Society.
Title: Advances in Graph Tomography
Deputy Director at AI Singapore and Associate Professor (Practice) at the NUS
Stefan Winkler is Deputy Director at AI Singapore and Associate Professor (Practice) at the National University of Singapore. Prior to that he was Distinguished Scientist and Director of the Video & Analytics Program at the University of Illinois’ Advanced Digital Sciences Center (ADSC) in Singapore. He also co-founded two start-ups and worked for a Silicon Valley company.
Dr. Winkler has a Ph.D. degree from the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and a Dipl.-Ing. (M.Eng./B.Eng.) degree from the University of Technology Vienna, Austria. He is an IEEE Fellow and has published over 130 papers and the book “Digital Video Quality.” He was Associate Editor of the IEEE Transactions on Image Processing (2009-2018) and the IEEE Signal Processing Magazine (Standards Column) (2012-2015). He is a member of the IVMSP Technical Committee of the IEEE Signal Processing Society, Executive Committee Member of the IEEE Singapore Section and the IEEE Singapore Signal Processing Chapter. He has served on the technical program committees of numerous conferences and workshops, including area chair of ICIP and ICME, as well as the QoMEX Steering Committee (2014-2017). He has also contributed to video quality standards in VQEG, ITU, ATIS, VSF, and SCTE. His research interests include video processing, computer vision, machine learning, perception, and human-computer interaction.
Title: AI with EQ – Helping Machines to Recognize Human Emotions
While most “AI” researchers focus mainly on the “IQ” aspect of intelligence, emotional intelligence or “EQ” is just as important for machines to be able to interact with humans effectively and naturally. In this talk, I will discuss our work on two projects where we explore the emotional aspects of visual analytics.
The first is photowork: Ubiquitous and affordable digital cameras have led to an explosion of the amount of image material both amateurs and professionals have to work with. Assessing, selecting, editing, organizing, annotating, and browsing this large amount of visual data is tedious and time-consuming. Our aim in this project is to automate some of these processes. Our approaches are content-based and focus on family photo collections, where people and their relationships play a major role. Our experiments highlight the importance of considering emotions for this purpose.
The second is on profiling people, with a focus on their affective states. Facial expressions in particular are an essential component for conveying and understanding human emotions. Contrary to most existing approaches in computer vision, we avoid the classification of emotions into a few predefined categories, and instead follow a dimensional paradigm as represented by the circumplex model. Based on the tracking of facial landmark points and relevant geometrical features, we directly estimate arousal, valence, and intensity of emotion. We discuss the benefits of our method, and also present some of its applications.
Defence Science and Technology, Australia
Neil Gordon received a PhD in Statistics from Imperial College London in 1993. He was with the Defence Evaluation and Research Agency in the UK from 1988-2002 working on missile guidance and statistical data processing. In 2002 he moved to the Defence Science and Technology Group in Adelaide, Australia where he is currently Research Leader for Intelligence Analytics. In 2014 he became an honorary Professor with the School of Information Technology and Electrical Engineering at the University of Queensland. He is the co-author/co-editor of two books on particle filtering and one on the search for MH370.
Title: Signal Processing and the search for MH370
Abstract: On 7th March 2014 Malaysian Airlines flight MH370 from Kuala Lumpur to Beijing lost contact with Air Traffic Control and was subsequently reported missing. An extensive air and sea search was made around the last reported location of the aircraft in the Gulf of Thailand without success. Signals transmitted by the aircraftâ€™s satellite communications terminal to Inmarsatâ€™s 3F1 Indian Ocean Region satellite indicated that the aircraft continued to fly for several hours after loss of contact. In this talk I will describe how nonlinear/non-Gaussian Bayesian time series estimation methods have been used to process the Inmarsat data and produce a probability distribution of MH370 flight paths that defined the search zone in the southern Indian Ocean. I will describe how probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. A particle filter based numerical calculation of the aircraft flight path probability distribution will be outlined and the method is demonstrated and validated using data from several previous flights of the accident aircraft. A short book is freely available for download from: http://www.springer.com/us/book/9789811003783