|Academic Profile |
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Prof Huang Guangbin
Professor, School of Electrical & Electronic Engineering
|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.”
|Extreme Learning Machine, Neuroscience, machine learning theories and algorithms, human-computer interface, data analytics, video analytics, robotics.|
- Measurement and Verification of Chilled - Water Plant
- Next Generation Broadband, Compact, Ultra-sensitive, Real-time, Tunable Laser Spectroscopy Analyzer
- L.L.C. Kasun, Y. Yang, G.B. Huang, & Z. Zhang. (2016). Dimension Reduction with Extreme Learning Machine. IEEE Transactions on Image Processing, 25, 3906-3918.
- J. Tang, C. Deng, & G.-B. Huang. (2016). Extreme Learning Machine for Multilayer Perceptron. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 809-821.
- G.-B. Huang, Z. Bai, L. L. C. Kasun, & C. M. Vong. (2015). Local Receptive Fields Based Extreme Learning Machine. IEEE Computational Intelligence Magazine, 10(2), 18-29.
- J. Tang, C. Deng, G.-B. Huang, & B. Zhao. (2015). Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine. IEEE Transactions on Geoscience and Remote Sensing, 53(3), 1174-1185.
- G. Huang, G.-B. Huang, S. Song, & K. You. (2015). Trends in Extreme Learning Machines: A Review. Neural Networks, 61(1), 32-48.