Keynote Speaker Ⅰ
Prof. Jianrong Tan
Institute of Design Engineering School of Mechanical Engineering, Zhejiang University, China
Academician of Chinese Academy of Engineering
A brief introduction to Prof. Jianrong Tan: Jianrong Tan is a mechanical engineering expert, master of engineering and doctor of science, distinguished professor of Zhejiang University. Mainly engaged in mechanical design and digital manufacturing research, and put forward the combination of batch and custom mass customization design technology, engineering transition state, fuzzy state, random state model and digital prototype integrated simulation technology, combination of numerical and geometric complicated equipment multi-unit association, multi-level configuration and parameter matching analysis technology, won 7 national awards, including national science and technology progress second prize 4 items, the national outstanding teaching achievements first prize 1 item, second prize 2 items, awarded provincial scientific and technological progress first prize seven items; Put into use the proposed technique in software, develop and access 12 computer software copyright which have successful applications in a number of manufacturing enterprises. He published 8 books, 142 papers in SCI/EI. In 2007, he was elected to the Chinese Academy of Engineering.
Keynote Speaker Ⅱ
Prof. Tingwen Huang
Texas A&M University, Qatar
Fellow of TWAS, Fellow of IEEE and IAPR
A brief introduction to Prof. Tingwen Huang: Tingwen Huang is a Professor at Texas A&M University at Qatar. He received his B.S. degree from Southwest Normal University (now Southwest University), China, 1990, his M.S. degree from Sichuan University, China, 1993, and his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then he joined Texas A&M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, then he was promoted to Professor in 2013. Dr. Huang’s research areas include neural networks, chaotic dynamical systems, complex networks, optimization and control, smart grid. He is a Fellow of The World Academy of Sciences (TWAS) for the advancement of science in developing countries, a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of IEEE and IAPR (International Association of Pattern Recognition), and Changjiang Chair Professor. He was conferred Outstanding Achievement Award by Asia Pacific Neural Networks Society, The Association of Former Students Distinguished Achievement Award for Research, one of the highest honors bestowed to a faculty of Texas A&M University in College Station, USA.
Keynote Speaker Ⅲ
Prof. Guangbin Huang
Nanyang Technological University, Singapore
A brief introduction to Prof. Guangbin Huang: He received the B.Sc degree in applied mathematics and M.Eng degree in computer engineering from Northeastern University, P. R. China, in 1991 and 1994, respectively, and Ph.D degree in electrical engineering from Nanyang Technological University, Singapore in 1999. During undergraduate period, he also concurrently studied in Wireless Communication department of Northeastern University, P. R. China. He is a Full Professor (with tenure) in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He is a member of Elsevier's Research Data Management Advisory Board. He is one of three Directors for Expert Committee of China Big Data Industry Ecological Alliance organized by China Ministry of Industry and Information Technology, and a member of International Robotic Expert Committee for China. He was a Nominee of 2016 Singapore President Science Award, was awarded Thomson Reuters’s 2014 “Highly Cited Researcher” (Engineering), Thomson Reuters’s 2015 “Highly Cited Researcher” (in two fields: Engineering and Computer Science), and listed in Thomson Reuters’s “2014 The World's Most Influential Scientific Minds” and “2015 The World's Most Influential Scientific Minds.” He received the best paper award from IEEE Transactions on Neural Networks and Learning Systems (2013). His two works on Extreme Learning Machines (ELM) have been listed by Google Scholar in 2017 as Top 2 and Top 7 respectively in its “Classic Papers: Articles That Have Stood The Test of Time” - Top 10 in Artificial Intelligence. He serves as an Associate Editor of Neurocomputing, Cognitive Computation, Neural Networks, and IEEE Transactions on Cybernetics. He was invited to give keynotes on numerous international conferences. His current research interests include big data analytics, human computer interface, brain computer interface, image processing/understanding, machine learning theories and algorithms, extreme learning machine, and pattern recognition. From June 1998 to May 2001, he worked as Research Fellow in Singapore Institute of Manufacturing Technology (formerly known as Gintic Institute of Manufacturing Technology) where he has led/implemented several key industrial projects and also built up two R&D labs: Communication Information Technologies Lab and Mobile Communication Lab. He was the chief architect for several significant industrial projects including (Singapore Airlines) SATS Cargo Terminal 5 Information Tracking System. He is Principal Investigator of BMW-NTU Joint Future Mobility Lab on Human Machine Interface and Assisted Driving, Principal Investigator (data and video analytics) of Delta – NTU Joint Lab, Principal Investigator (Scene Understanding) of ST Engineering – NTU Corporate Lab, and Principal Investigator (Marine Data Analysis and Prediction for Autonomous Vessels) of Rolls Royce – NTU Corporate Lab. He has led/implemented several key industrial projects (e.g., Chief architect/designer and technical leader of Singapore Changi Airport Cargo Terminal 5 Inventory Control System (T5 ICS) Upgrading Project, etc). One of his main works is to propose a new machine learning theory and learning techniques called Extreme Learning Machines (ELM), which fills the gap between traditional feedforward neural networks, support vector machines, clustering and feature learning techniques. ELM theories have recently been confirmed with biological learning evidence directly, and filled the gap between machine learning and biological learning. ELM theories have also addressed “Father of Computers” J. von Neumann’s concern on why “an imperfect neural network, containing many random connections, can be made to perform reliably those functions which might be represented by idealized wiring diagrams.”
Keynote Speaker Ⅳ
Prof. Christos G. Cassandras
Boston University, USA
Fellow of IEEE, IFAC
Paper Title: Optimal Safe-critical Autonomy for Multi-agent Systems: Making Autonomous Vehicles a Reality
Abstract: Implementing solutions to complex dynamic optimization problems is limited by the fact that these solutions must often satisfy hard safety constraints at all times. A prime example arises in optimizing the operation of autonomous mobile robots (e.g., autonomous vehicles) which must, above all else, guarantee safety. Obtaining such solutions incurs a high computational cost, which limits them to models with simple linear dynamics, simple objective functions, and ignoring noise. Control Barrier Functions (CBFs) may be used for safety-critical control overcoming such limitations at the expense of sub-optimal performance. We present a real-time control framework that combines trajectories generated through optimal control with the computationally efficient CBF method providing safety guarantees. A tractable optimal solution is first obtained for a linear or linearized system with few or no constraints. Next, this solution is optimally tracked while using CBFs to guarantee the satisfaction of all state and control constraints. This Optimal Control and CBF (OCBF) framework is applied to autonomous vehicles in transportation systems where the objective is to jointly minimize the travel time and energy consumption for each vehicle subject to speed, acceleration, and speed-dependent safety constraints. We will also present some recent new results showing that this framework can be robust to the behavior of human-driven vehicles despite their uncontrollable and unpredictable behaviors.
A brief introduction to Prof. Christos G. Cassandras: Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received a B.S. degree from Yale University, M.S.E.E from Stanford University, and S.M. and Ph.D. degrees from Harvard University. In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 500 refereed papers in these areas, and seven books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents. Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He is currently an Editor of Automatica. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001, the IEEE Conference on Decision and Control in 2002 and 2016, and the 20th IFAC World Congress in 2017 and has also been an IEEE Distinguished Lecturer. He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a “Smart Parking” system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several outstanding paper awards, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC and holds a Chair Professorship at the Department of Automation, Tsinghua University.
Keynote Speaker Ⅴ
Assoc. Prof. Wei Pan
University of Manchester, UK
A brief introduction to Assoc. Prof. Wei Pan: He is a Senior Lecturer (Associate Professor) in Machine Learning at the Department of Computer Science and a member of Centre for AI Fundamentals and Centre for Robotics and AI, The University of Manchester, UK. Before that, he was an Assistant Professor in Robot Dynamics at the Department of Cognitive Robotics and co-director of Delft SELF AI Lab, TU Delft, Netherlands and a Project Leader at DJI, China. He is an Area Chair or Associate Editor of IEEE Robotics and Automation Letters, ACM Transactions on Probabilistic Machine Learning, Conference on Robot Learning (CoRL), IEEE International Conference on Robotics and Automation (ICRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). He received his degrees from Imperial College London, University of Science and Technology of China and Harbin Institute of Technology.