Academic Profile : Faculty

Ong Yew Soon-v2.jpg picture
Prof Ong Yew Soon
Co-Director, Singtel Cognitive and Artificial Intelligence Lab (SCALE@NTU)
President’s Chair in Computer Science
Professor, School of Computer Science and Engineering
Professor, School of Physical & Mathematical Sciences (Courtesy Appointment)
External Links
Yew-Soon Ong is currently a President's Chair Professor of Computer Science at the School of Computer Science and Engineering and Professor (Cross Appointment) of the School of Physical and Mathematical Science at Nanyang Technological University (NTU), Singapore. At the same time, he is Chief Artificial Intelligence (CAS) Scientist of the Singapore's Agency for Science, Technology and Research (A*STAR). At NTU, he serves as co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab (SCALE@NTU). He was Chair of the School of Computer Science and Engineering, Nanyang Technological University from 2016-2018, Director of the Centre for Computational Intelligence/Computational Intelligence Laboratory from 2008-2015 and Programme Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab from 2013-2017. He received his Bachelors and Masters degrees in Electrical and Electronics Engineering (Specializing in Computing) from Nanyang Technological University and subsequently his PhD (Thesis Title: Artificial Intelligence in Complex Engineering Design) from the University of Southampton, United Kingdom. He is a Fellow of IEEE and founding Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, founding Technical Editor-In-Chief of Memetic Computing Journal (Springer), Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Big Data (2015 - 2018), IEEE Computational Intelligence Magazine (2010 - 2016), and chief co-editor of Book Series on Studies in Adaptation, Learning, and Optimization.
His current research interests include Artificial & Computational Intelligence spanning across Memetic Computation, Machine Learning, Evolutionary & Transfer Optimization and Complex Engineering Design. His research grants comprises of external funding from both national and international partners that include Boeing Research & Development (USA), Rolls-Royce (UK) and Honda Research Institute Europe (Germany), the National Research Foundation of Singapore, National Grid Office, A*STAR, MDA-GAMBIT, AISG, NRF, Singapore Technologies, Singtel, NCS and other. His research on Memetic Computation was first featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in 2007. He was subsequently listed as a Thomson Reuters Highly Cited Researcher and among the World's Most Influential Scientific Minds. He received the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and more recently the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his works in Memetic Computation.
  • "Design Beyond What You Know”: Material-Informed Differential : Generative AI (MIDGAI) for Light-Weight High-Entropy Alloys and Multi- functional Composites (Stage 1a)
  • “Design Beyond What You Know”: Material-Informed Differential Generative AI (MIDGAI) for Light-Weight High-Entropy Alloys and Multi-functional Composites (Stage 1a)
  • Accelerating traditional workflows in animation and gaming through creative AI solutions
  • Distributed Smart Value Chain (DSVC)
  • Fusion Science for Clean Energy
  • Generative AI for Complex Design Optimization and Cooperative Creativity
  • Learning Assisted Human-AI Collaboration for Large-scale Practical Combinatorial Optimization
  • President's Chair in Computer Science
  • Transformative mobile learning in a first-year interdisciplinary writing class
US-2010-0106714-A1: Method and Apparatus for Automatic Configuration of Meta-Heuristic Algorithms In A Problem Solving Environment (2015)
Abstract: A methodology is presented to address the need for rapid generation and optimization of algorithms that are efficient in solving a given class of problems within the framework of a software environment. The environment incorporates an evolutionary learning methodology which automatically optimizes the configurations of procedural components of the algorithm. In this way, both the efficiency and the quality of algorithm development is enhanced significantly.