IEEE Fellow, IET Fellow, Fellow of the United Kingdom Royal Academy of Engineering
Professor. Dr., University of Southampton, United Kingdom
Sheng received the BEng degree in control engineering from the East China Petroleum Institute in January 1982 and the PhD degree in control engineering from the City University at London in September 1986. In 2005, he was awarded the higher doctoral degree, Doctor of Sciences (DSc), by the University of Southampton.
He joined the School of Electronics and Computer Science, the University of Southampton in September 1999, where he currently holds the post of Professor in Intelligent System and Signal Processing. He previously held research and academic appointments at the Universities of Sheffield, Edinburgh and Portsmouth. He is a Distinguished Visiting Professor at King Abdulaziz University, Jeddah, Saudi Arabia. Professor Chen is a Chartered Engineer (CEng), a Fellow of IET (FIET), and a Fellow of IEEE (FIEEE). He was elected to a Fellow of the United Kingdom Royal Academy of Engineering in 2014.
Sheng's research interests are in learning theory and neural networks, adaptive signal processing for communications, wireless communications, modelling and identification of nonlinear systems, evolutionary computation methods and optimisation.
Professor. Dr., Virginia Commonwealth University, USA
Dr. Manic is a Professor with Computer Science Department at Virginia Commonwealth University. He has over 20 years of academic and industrial experience. His previous positions include tenured positions with University of Idaho, director of the Computer Science Program at Idaho Falls, University of Nis, Serbia, and a Fellow of the Brain Korea 21 Program. He has completed over 30 research efforts in the area of data mining and machine learning applied to energy optimization, resilient control, and human-machine interaction. Dr. Manic has given over 30 invited talks around the world, authored over 180 refereed articles in international journals, books, and conferences, holds several U.S. patents and has won 2018 R&D 100 Award. Dr. Manic is an IEEE Industrial Electronics Society (IES) Officer and is a member of various standing and technical committees and boards of this Society. He is also involved in various capacities in Technical Committees on Education, Industrial Informatics, Factory Automation, Smart Grids, Standards, and Web and Information Committee, and is a co-founder and past chair of Technical Committee on Resilience and Security in Industry, and a general chair of IEEE IECON 2018, IEEE HSI 2019.
Michele Della Ventura
Professor. Dr., Music Academy 'Studio Musica', Italy
Michele Della Ventura, professor of Music Technology, is a learning expert, researcher and instructional designer. His research interests include correlation between music and mathematics with a particular emphasis on artificial intelligence research in the field of computer-aided analysis of tonal music; intelligent systems; enhancing teaching and learning with technology; assessment for learning and strategies and models for the effective integration of technology into the curriculum at all academic levels.
He is the author of several articles presented at many conferences and published in international science magazines and high school textbooks (also featured at the International Book Salon of Turin in 2012 and 2018).
He proofreads articles and is a member of scientific committees in International Conferences.
He was invited as keynote speaker to International Conferences in Italy, Austria, Canada, China, Czech Republic, France, Germany, Hong Kong, Hungary, Ireland, Japan, Norway, Poland, Portugal, Romania, Singapore, Spain, UK, US (Baltimora, Boston, Las Vegas, New York, Washington).
Michele Della Ventura has also consulted on Big Data and Semantic Technology projects in Italy. Some of the projects include indexation of the symbolic level of musical text.
He is currently involved in a research project related to technology supported learning in collaboration whit Università di Roma La Sapienza.
He teaches Music Informatics in University courses at Music Academies and Conservatories and Musical Technologies in Music High Schools.
Speech Title: E-Teacher vs E-Student
Chair/Professor. Dr., University of Portsmouth, the United Kingdom
Dr. Hui Yu is a Chair/Professor with the University of Portsmouth in the UK. He is the Head of the Visual Computing Group at the university. His main research interest lies in visual computing and big data analysis, particularly in understanding and sensing the visual world of human related issues with semantic interpretation. It involves and develops knowledge and technologies in vision, machine learning, virtual reality, brain-computer interaction and robotics. Professor Yu's research work has led to many awards and successful collaboration with worldwide institutions and industries. He has led projects supported by EPSRC, ESRC, Royal Academy of Engineering, EU-FP7 and industries. He has extensive contributions to the international research community with organizing and chairing international research conferences and summer schools. He is also Associated Editor of IEEE Transactions on Human-Machine Systems journal and Neurocomputing journal.
Speech Title: Deep learning application and facial analysis on data in the wild
Abstract: Video analysis has been an active research field with popular applications. The vast demands and advancement of technologies have enabled a wide range of applications of sensing systems for capturing facial performance and affective states. With the availability of the complex facial data, a large body of research has been conducted in the past decades. However, there are still significant challenges in this area due to new applications and higher accuracy demands. In-the-wild facial data under unconstrained conditions pose a big challenge to existing facial analysis approaches. In this talk we will address the challenges and some new solutions to facial and gaze analysis on wild data. We will also discuss the fundamental techniques in face frontalization, facial landmark localization and gaze estimation with their applications.