Dr. Yew Soon Ong Home Page

	Located at	Teaching
	School of Computer Science and Engineering Nanyang Technological University Block N4, 2a-28, Nanyang Avenue, Singapore 639798 Tel: +65-6790-6448 Fax: +65-6792-6559	Present Courses RE1016: Engineering Computation Renaissance Engineering Programme Previous Courses Taught CZ4042-CPE422-CSC422 Artificial Neural Networks (Prescribed Elective) CSC207 Software Engineering, Year 2 Core SC206 Microprocessor Systems Design SC302/CPE302 Computer Networks, Year 3 Core BI6121 High Performance Computing in Bioinformatics, Master of Science (Bioinformatics)
Yew-Soon Ong President's Chair Professor of Computer Science BEng MEng PhD Dr.- FIEEE	Email To
	asysong<@>ntu.edu.sg
Research Interest: Artificial & Computational Intelligence, Machine Learning, Evolutionary & Memetic Computation, Transfer & Bayesian Optimization Yew-Soon Ong is currently President's Chair Professor of Computer Science at the School of Computer Science and Engineering 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 (ASTAR). At NTU, he serves co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab (SCALE@NTU), co-Director of the AStar SIMTECH-NTU Joint Lab on Complex Systems. He was Chair of the School of Computer Science and Engineering, Nanyang Technological University from 2016-2018, Director of the Data Science and Artificial Intelligence Research Center (DSAIR) from 2016-2021, 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 , Associate Editor of IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Artificial Intelligence, and chief co-editor of Book Series on Studies in Adaptation, Learning, and Optimization. His current research interests include Artificial & Computational Intelligence spanning Memetic Computation, Evolutionary & Transfer Optimization and Machine Learning. 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, ASTAR, Singapore Technologies Dynamics and MDA-GAMBIT. 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 August 2007. He was listed as a Thomson Reuters Highly Cited Researcher in 2015 and 2016 and among the World's Most Influential Scientific Minds. He also received the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and very recently the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his works in Memetic Computation. Several of his technologies in Artificial & Computational Intelligence research have been commercialized and licensed to companies and institutions, where he received the Research Commercialization award in 2015. An overview of his research & technological showcase is available 'Here'. His crowd intelligence research has led to the AI-enabled IOS 'Dark-Dots Game'. It was the top action game in 48 countries including USA, China and Singapore; downloaded by well over 448,000 players worldwide when launched, with 27% of its players from China and 17% from the USA. The success of the AI research-enabled IOS DarkDot Game has led to the Inzen Studio Pte. Ltd. Startup which subsequently gave rise to the newly commercialized DarkDots'. Research Statement*: The technology behind the core game mechanic of Dark Dot is the Flocking Animation and Modelling Environment (FAME). FAME is a method in which multiple units or agents react together as a flock or swarm in a consistent manner, based on inputs involving shape, space and time. The research was funded by GAMBIT MDA-NRF with Dr. Ong as the Principal Investigator and is a co-Inventor of the technology. FAME was also commercialized worldwide as a crowd intelligence API through the Unity Asset Store in 2014. More than 130 licenses have been snap-up within two months of its release. Other successful translations of technologies in AI include the: "Algorithm Development Environment for Problem Solving", a Patented AI training AI systems and the "Large-Scale Engineering Simulation for Complex Adaptive Systems (LesCaS)", a decision support system designed for large scale modelling, simulation and optimization of complex systems, which was also transferred through licensing to the industry. In teaching, he has also received a number of awards including the Prestigious Nanyang Education Award (University) in 2016, Nanyang Education Award (College of Engineering) in 2015, Nanyang Excellence Award (School of Computer Science & Engineering) in 2008, Most Popular Lecturer Award 2009. He was featured in the 2014 Nanyang Chronicle for introducing new waves of pedagogical and innovative use of technology to improve teaching, while featured in the Bright Minds Magazine as a qualified faculty that transforms students into promising gems in 2009. He is as Fellow of Renaissance Engineering Programme and committee member of the Teaching Council at Nanyang Technological University.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, IEEE CIS, Founding Editor-in-Chief: Yew-Soon Ong IEEE TEVC Special Issue on "Multi-task Evolutionary Computation "Call for Papers", Guest Editors: Abhishek Gupta, Singapore Institute of Manufacturing Technology, Singapore, Yew-Soon Ong, Nanyang Technological University, Singapore, Kenneth De Jong, George Mason University, USA, Mengjie Zhang, Victoria University of Wellington, New Zealand. Submission open: May 1, 2020, Submission Deadline: November 1, 2020. 'Memetic Computation: The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era', 2019 New Book by Authors: Abhishek, Gupta and Yew-Soon, Ong Abstract: This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). Available here: For Download. *"AIR5: Five Pillars of Artificial Intelligence Research", IEEE Transactions on Emerging Topics in Computational Intelligence, 2019. Available here: PDF file. Selected Refereed Publications* ARTIFICIAL INTELLIGENCE - MACHINE LEARNING J. Xie, X. Zhan, Z. Liu, Y. S. Ong and C. C. Loy, “Unsupervised Object-Level Representation Learning from Scene Images ”, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021). Available here as PDF file. T. T. He, Y. S. Ong and L. Bai, “Learning Conjoint Attentions for Graph Neural Nets ”, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021). J. C. Wong, A. Gupta and Y. S. Ong, “Can Transfer Neuroevolution Tractably Solve Your Differential Equations? ”, IEEE Computational Intelligence Magazine, In Press, 2021. Available here as PDF file. Source code available here tNES. L. Bai, W. Lin, A. Gupta and Y. S. Ong, “From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking: A Proof of Faster Convergence”, IEEE Transactions on Cybernetics, In Press, 2021. Available here as PDF file. A. Chan and Y. S. Ong, B. Pung, A. Zhang, J. Fu,CoCon: A Self-Supervised Approach for Controlled Text Generation ”, The International Conference on Learning Representations (ICLR-2021), 4-8 May, 2021. X. Qu, Y. S. Ong and A. Gupta, “Frame-Correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies”, IEEE Transactions on Cybernetics, In Press, 2021. A. Chan, Y. Tay and Y. S. Ong, “What it Thinks is Important is Important: Robustness Transfers through Input Gradients”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2020), 16-18 June, 2020, Seattle, Washington. P. Wei, R. Sagarna, Y. Ke and Y. S. Ong, “Easy-but-effective Domain Sub-similarity Learning for Transfer Regression”, IEEE Transactions on Knowledge and Data Engineering, In Press, 2020. X. Zhan, J. Xie, Z. Liu, Y. S. Ong and C. C. Loy, “Online Deep Clustering for Unsupervised Representation Learning”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2020), 16-18 June, 2020, Seattle, Washington. A. Chan, Y. Tay, Y. S. Ong and J. Fu, “Jacobian adversarially regularized networks for robustness”, The International Conference on Learning Representations (ICLR-2020), 26-30 April, 2020, Millennium Hall, Addis Ababa Ethiopia. H. T. Liu, Y. S. Ong, X. Shen, and J. F. Cai, “When Gaussian Process Meets Big Data: A Review of Scalable GPs”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 11, 2020. Available here as PDF file. Y. S. Ong and A. Gupta, “AIR5: Five Pillars of Artificial Intelligence Research”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 3, No. 5, pps. 411 - 415, 2019. Available here as PDF file B. Da, A. Gupta, Y. S. Ong, “Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization”, IEEE Transactions on Cybernetics, Vol. 49, No. 12, pps. 4365-4378, 2019. Paper available here as PDF file. Source code available at Github. H. Liu, J. F. Cai, Y. Wang and Y. S. Ong, “Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression”, 35th International Conference on Machine Learning (ICML 2018), July 10-15, 2018, Stockholm, Sweden. X. Shen, S. Pan, W. Liu, Y. S. Ong and Q. S. Sun, “Discrete Network Embedding ”, 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), July 13-19, 2018, Stockholm, Sweden. X. Shen, W. Liu, Y. Luo, Y. S. Ong and I. W. Tsang, “Deep Binary Prototype Multi-label Learning ”, 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), July 13-19, 2018, Stockholm, Sweden. W. M. Tan, Y. S. Ong, A. Gupta, et al., “Multi-Problem Surrogates: Transfer Evolutionary Multiobjective Optimization of Computationally Expensive Problems”, IEEE Transactions on Evolutionary Computation, In Press, 2018. X. Shen, W. Liu, I. Tsang, Q.S. Sun and Y. S. Ong , “Compact Multi-label Learning”, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Feb 2-7, 2018, New Orleans, Lousiana, USA. W. M. Tan, R. Sagarna, A. Gupta, Y. S. Ong, et al., “Knowledge Transfer through Machine Learning in Aircraft Design”, IEEE Computational Intelligence Magazine, In Press, 2017. P. Wei, R. Sagarna, Y. Ke, Y. S. Ong, , et al., “Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression”, International Conference on Machine Learning (ICML 2017), August 6-11, 2017. H. Yang, J. T. Zhou, J. Cai and Y. S. Ong, “MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, July 21-26, 2017. Y. Zhai, Y. S. Ong, and I. W. Tsang, "Making Trillion Correlations Feasible in Feature Grouping and Selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 12, pp. 2472-2486, 2016. Available here as PDF file Y. Zhai, Y. S. Ong, and I. W. Tsang, "The Emerging Big Dimensionality", IEEE Computational Intelligence Magazine, Vol. 9, No. 3, pp. 14-26, 2014. Available here as PDF file. C. W. Seah, I. W. Tsang and Y. S. Ong, and I. W. Tsang, "Transfer Ordinal Label Learning", IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 11, pps. 1863-1876, 2013. Available here as PDF file. C. W. Seah, Y. S. Ong, and I. W. Tsang, "Combating Negative Transfer from Predictive Distribution Differences", IEEE Transactions On Cybernetics, No. 99, pps. 1-13, 2013. Available here as PDF file Y. Zhai, M. K. Tan, I. W. Tsang and, Y. S. Ong, "Discovering Support and Affiliated Features from Very High Dimensions", International Conference on Machine Learning (ICML 2012), June 2012. EVOLUTIONARY & MEMETIC COMPUTATION (Theory, Algorithms, Survey & Applications) L. Bai, W. Lin, A. Gupta and Y. S. Ong, “From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking: A Proof of Faster Convergence”, IEEE Transactions on Cybernetics, In Press, 2021. K. K. Bali, A. Gupta, Y. S. Ong and P. S. Tan, "Cognizant Multitasking in Multi-Objective Multifactorial Evolution: MO-MFEA-II", IEEE Transactions on Evolutionary Computation, In Press, 2019. Available here: PDF file. K. K. Bali, Y. S. Ong, A. Gupta and P. S. Tan, "Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-II", IEEE Transactions on Evolutionary Computation, Vol. 24, No. 1, 2020. Available here: PDF file. A. Gupta and Y. S. Ong "Back to the Roots: Multi-X Evolutionary Computation", Cognitive Computation, vol. 11, pps. 1-17, 2019. Available here: PDF file. A. Gupta, Y. S. Ong, and L. Feng "Insights on Transfer Optimization: Because Experience is the Best Teacher", IEEE Transactions on Emerging Topics in Computational Intelligence, Vol.2, No. 1, pps. 51 - 64, 2018. Available here: PDF file. L. Feng, Y. S. Ong, S. Jiang and A. Gupta, "Autoencoding Evolutionary Search with Learning across Heterogeneous Problems", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 5, pps. 760 - 772, 2017. Available here as PDF file. Y. Zeng, X. Chen, Y. S. Ong, J. Tang and Y. Xiang, "Structured Memetic Automation for Online Human-like Social Behavior Learning", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pps. 102-115, 2017. Available here as PDF file. Y. S. Ong, and A. Gupta, "Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking", Cognitive Computation, Vol. 8, No. 2, pps. 125-142, 2016. Available here as PDF file. A. Gupta, Y. S. Ong, L. Feng and K. C. Tan, "Multi-Objective Multifactorial Optimization in Evolutionary Multitasking", IEEE Transactions on Cybernetics, Accepted 2016. Available here as PDF file. A. Gupta, Y. S. Ong, and L. Feng, "Multifactorial Evolution: Towards Evolutionary Multitasking", IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 343 - 357, 2016. Available here as PDF file. Source code Download. . Bestowed the 2019 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award. Y. S. Ong, L. Feng, A.K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, and X. Yao, "Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results", Technical Report, 2016. Available here as PDF file Y. Yuan, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta., B. Da, Q. Zhang, K. C. Tan, Y. Jin, and H. Ishibuchi, "Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results", Technical Report, 2016. Available here as PDF file. "For more info on 'Multifactorial Evolution- Evolutionary Multitasking', Benchmark Problems, Publications and Source Codes Downloads, Click here!"* L. Feng, Y. S. Ong, A. H. Tan and I. W. Tsang, "Memes as Building Blocks: A Case Study on Evolutionary Optimization + Transfer Learning for Routing Problems", Memetic Computing, vol. 7, no. 3, pp. 159-180, 2015. Available here as PDF file. L. Feng, Y. S. Ong, M.-H. Lim, and I. W. Tsang, "Memetic Search with Inter-Domain Learning: A Realization between CVRP and CARP", IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. Oct 2015. Available here as PDF file. M. N. Le, Y. S. Ong, Y. Jin and B. Sendhoff, "A Unified Framework for Symbiosis of Evolutionary Mechanisms with Application to Water Clusters Potential Model Design", IEEE Computational Intelligence Magazine, Vol. 7, No. 1, pp. 20 - 35, 2012. Bestowed the 2015 IEEE CIS Outstanding Computational Intelligence Magazine Paper Award. Available here as PDF file. X. S. Chen, Y. S. Ong, M. H. Lim and K. C. Tan, "A Multi-Facet Survey on Memetic Computation", IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 591 - 607, Oct 2011. Available here as PDF file. Y. S. Ong, M. H. Lim and X. S. Chen, "Research Frontier: Memetic Computation -* Past, Present & Future", IEEE Computational Intelligence Magazine, Vol. 5, No. 2, pp. 24 -36, 2010. Available here as PDF file. Q. H. Nguyen, Y. S. Ong and M. H. Lim, “A Probabilistic Memetic Framework”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 604-623, June 2009. Bestowed the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award Available here as PDF file or at IEEE Xplore as PDF file. Source code Download. M. N. Le, Y. S. Ong, Y. Jin & B. Sendhoff, 'Lamarckian memetic algorithms: local optimum and connectivity structure analysis', Memetic Computing* , Vol. 1, No. 3, pp. 175-190, 2009. Available here as PDF file. Source code Download. Z. Zhu, Y. S. Ong and M. Dash, “Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework”, IEEE Transactions On Systems, Man and Cybernetics - Part B, vol. 37, no. 1, pp. 70-76, Feb 2007. Available here as PDF file. *Source code Download. Y. S. Ong, M. H. Lim, N. Zhu and K. W. Wong, “Classification of Adaptive Memetic Algorithms: A Comparative Study”, IEEE Transactions On Systems, Man and Cybernetics - Part B, Vol. 36, No. 1, pp. 141-152, February 2006. Available here as PDF file.* Y. S. Ong and A.J. Keane, “Meta-Lamarckian Learning in Memetic Algorithm”, IEEE Transactions On Evolutionary Computation, Vol. 8, No. 2, pp. 99-110, April 2004. *Featured by Thomson Scientific's Essential Science Indicators as one of the most cited papers in August 2007. Available here as PDF file. EVOLUTIONARY OPTIMIZATION meets MACHINE LEARNING W. M. Tan, R. Sagarna, A. Gupta, Y. S. Ong, , et al., “Knowledge Transfer through Machine Learning in Aircraft Design”, IEEE Computational Intelligence Magazine, In Press, 2017, PDF file. A. Kattan, A. Agapitos,Y. S. Ong, A. A. Alghamedi and M. O'Neill, “GP Made Faster with Semantic Surrogate Modelling”, Information Sciences, Vol. 355-356, pps. 169-185, 2016. J. H. Zhong, Y. S. Ong and W. T. Cai, “Self-Learning Gene Expression Programming”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 65-80, 2016. L. Feng, Y. S. Ong, A. H. Tan and I. W. Tsang, "Memes as Building Blocks: A Case Study on Evolutionary Optimization + Transfer Learning for Routing Problems", Memetic Computing, vol. 7, no. 3, pp. 159-180, 2015. Available here as PDF file. L. Feng, Y. S. Ong, M.-H. Lim, and I. W. Tsang, "Memetic Search with Inter-Domain Learning: A Realization between CVRP and CARP", IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. Oct 2015. Available here as PDF file. A. Kattan and Y. S. Ong, “Surrogate Genetic Programming: A Semantic Aware Evolutionary Search”, Information Science, Vol. 296, pps. 345-359, 2015. M. N. Le, Y. S. Ong, S. Menzel, Y. Jin and B. Sendhoff, “Evolution by Adapting Surrogates”, Evolutionary Computation Journal, Vol. 1, No. 2, pps. 313-340, 2013. Available here as PDF file. S.D. Handoko, C.K. Kwoh and Y. S. Ong, "Feasibility Structure Modeling: An Effective Chaperon for Constrained Memetic Algorithms", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5, pp. 740-758, Jun 2010. Available here as PDF file D. Lim, Y. Jin, Y. S. Ong and B. Sendhoff, "Generalizing Surrogate-assisted Evolutionary Computation", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 329-355, Jun 2010. Available here as PDF file. *Source code Download. Z. Z. Zhou, Y. S. Ong, P. B. Nair, A. J. Keane and K. Y. Lum, “Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization”, IEEE Transactions On Systems, Man and Cybernetics - Part C, Vol. 37, No. 1, Jan. 2007, pp. 66-76. Available here as PDF file.* Y. S. Ong, P. B. Nair and K. Y. Lum, “Max-Min Surrogate-Assisted Evolutionary Algorithm for Robust Design”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 392-404, August 2006. Available here as PDF file. Y. S. Ong, P.B. Nair and A.J. Keane, 'Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling', American Institute of Aeronautics and Astronautics Journal, 2003, Vol. 41, No. 4, pp. 687-696. Available here as PDF file. Source code Download Springer-Verlag Book Series: 'STUDIES IN ADAPTATION, LEARNING, AND OPTIMIZATION', Chief co-editor, Yew-Soon Ong. MEMETIC COMPUTING, Science Citation Index Expanded, Springer-Verlag, Founding Technical co-Editors-in-Chief: Yew-Soon Ong
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