Guang-Bin Huang

— Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

— Founder of Extreme Learning Machines

Big data analytics, human computer interface, brain computer interface, image processing/understanding, machine learning theories and algorithms, extreme learning machine, and pattern recognition
Guang-Bin Huang 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 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). 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. 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.”