Ong, Yew-Soon - Nanyang Technological University - School of Computer Science and Engineering

个人简介

Yew-Soon Ong is currently 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 Director of the Data Science and Artificial Intelligence Research Center (DSAIR), co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab (SCALE@NTU), co-Director of the A*Star 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 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.

研究领域

Artificial & Computational Intelligence, Memetic Computing, Evolutionary & Bayesian Optimization (Transfer Optimization, Evolutionary Multitasking, Multifactorial Optimization), Machine Learning

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

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. 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, In Press, 2020. 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. Hu, Y. Luo, Y. Wen, Y. S. Ong and X. Zhang, “How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach”, IEEE Access, Vol. 6, No.1, pps. 60380-60395, Dec 2018. 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, and C. K. Goh, , “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, and C. K. Goh, , “Knowledge Transfer through Machine Learning in Aircraft Design”, IEEE Computational Intelligence Magazine, In Press, 2017. P. Wei, R. Sagarna, Y. Ke, Y. S. Ong, and C. K. Goh, , “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.