Biao Huang obtained his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He had MSc degree (1986) and BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering, and is currently a Full Professor, NSERC Senior Industrial Research Chair. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of Germany’s Alexander von Humboldt Research Fellowship and a best paper award from the Journal of Process Control. Biao Huang’s research interests include: process data analytics, machine learning, system identification, image processing, fault detection and isolation, and soft sensors. He has applied his expertise extensively in industrial practice. He has published 5 books and over 390 SCI journal papers. Biao Huang currently serves as the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, Associate Editor for Journal of Process Control.

Speech Title: Bayesian Inference – Control Engineering Perspective
Abstract: Bayesian theory, due to its mathematical rigor and application flexibility, has attracted great interests from both academic researchers and industrial practitioners. The original Bayesian theory, as a single formula, can evolve into pages of long mathematical derivations. Yet the end result provides very meaningful solutions to the practical problems. Bayesian inference has long application history in control engineering. The most well-known application of Bayesian theory is Kalman filter which has been widely adopted in control engineering applications. It is now commonly recognized that many practical problems may be formulated mathematically under Bayesian framework and readily solved. Bayesian inference is getting even more popular due to the growing interest in Data Science. This presentation will give a historical overview of Bayesian inference in control engineering applications and its current and future trends.

Graziano Chesi is a Professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong. He received the Laurea in Information Engineering from the University of Florence and the PhD in Systems Engineering from the University of Bologna. Before joining the University of Hong Kong, he was with the Department of Information Engineering of the University of Siena. He served as Associate Editor for various journals, including Automatica, the European Journal of Control, the IEEE Control Systems Letters, the IEEE Transactions on Automatic Control, the IEEE Transactions on Computational Biology and Bioinformatics, and Systems and Control Letters. He also served as chair of the Best Student Paper Award Committees for the IEEE Conference on Decision and Control and the IEEE Multi-Conference on Systems and Control. He is author of the books "Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems" (Springer 2009) and "Domain of Attraction: Analysis and Control via SOS Programming" (Springer 2011). He was elevated to IEEE Fellow for contributions to control of nonlinear and multi-dimensional systems.

Speech Title: LMI-Based Multiple-View Triangulation for Generalized Cameras
Abstract: Multiple-view triangulation is a fundamental problem in computer vision, which consists of estimating the position of a scene point by exploiting its image projections on several cameras. This talk considers the situation where the scene point is observed by generalized cameras, i.e., cameras that can be modeled by a spherical projection followed by a perspective one. Indeed, these cameras can have a much larger field of view than traditional pinhole cameras and, hence, they may be preferable in various applications. It is shown that convex optimization problems with linear matrix inequalities can be formulated to obtain the sought estimate, in particular, by introducing a criterion which consists of minimizing the angles between the projections on the sphere of the available image projections and the corresponding projections of the estimate.

Prof. Jimmy Liu
Southern University of Science and Technology, China

Prof. Jimmy Liu graduated from the Department of Computer Science of the University of Science and Technology of China in 1988. He further obtained his master and doctoral degrees in Computer Science from the National University of Singapore. In 2004, he started the Intelligent Medical Imaging Research Team (iMED Singapore, A*STAR) and grew it to become one of the world's largest ophthalmic medical image processing team, focusing on ophthalmic Artificial Intelligence research. Jimmy was the chairman of the IEEE Singapore Biomedical Engineering Society in Singapore.
In March 2016, Jimmy returned to China and founded the iMED China (Ningbo) team. He was the senior professor and founding director of the Cixi Institute of Biomedical Engineering (CIBE) of the Chinese Academy of Sciences.
In February 2019, he joined the Department of Computer Science and Engineering of the Southern University of Science and Technology to establish iMED Chi (Shenzhen). He is devoting his time to more fundamental eye-brain imaging, Artificial Intelligence, precision medicine, and surgical robotics research.

Speech Title: Intelligent Ocular Imaging Research and IMED Team latest research update 2021
Abstract: In the talk, Jimmy will update the ocular imaging research work in the past years. He will share his AI-based eye image processing work on various ocular imaging modalities. He will cover the following 4 areas conducted in IMED Team (Singapore, Ningbo and Shenzhen): ocular disease screening, robot assisted eye micro-surgery, ocular biometrics, as well as ocular medical informatics using genome study. He will introduce the current issues, technologies and approaches in this inter-disciplinary research area, and introduce his latest research work in 2020/2021 in details.