Tan, Ying is a professor in the Department of Machine Intelligence, School of EECS, Peking University and has served as a PhD advisor and the Director of Computational Intelligence Laboratory. He is the inventor of Fireworks Algorithm (FWA). He obtained his B.Eng. from Electronic Engineering Institute in 1985, and M.Eng. from Xidian University in 1988, and Ph.D. from Southeast University in 1997, respectively. His research interests include computational intelligence, swarm intelligence, machine learning and data mining, and information security application. Dr. Tan has published 6 monographs by Morgan Kaufmann (Elsevier), Springer, Wiley & IEEE, CRC Press/Taylor & Francis, IGI Global and Science Press, respectively, and published more than 260 research papers, and most of them are published in top-tier journals and conferences, such as IEEE TEC, TNN, TKDE, TCyb, TIFS, TITS, and CVPR and AAAI. In addition, he authored/co-authored more than 10 books and 12 chapters in book. He has served as a Leading Editor to coordinate and edit more than twenty volumes of Springer LNCS books. He got four invented patents of China. He was awarded a National 2nd-Class Natural Science Prize of China in 2009. He is/was the founder and general chairs of ICSI conferences since 2010. He is a general chair of DMBD2016-2017, ICMEB2017 and ICMEB2017. He was the program chair of IEEE WCCI’2014, ISNN2008 and ICACI2012, and publicity chair of IEEE SSCI’2016, etc.

Dr. Tan is an associate editor of IEEE Transactions on Cybernetics (2013-), IEEE Transactions on Neural Networks and Learning Systems (2015-) and IEEE Transactions on Evolutionary Computation (2017-), International Journal of Swarm Intelligence Research (IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He was an associate editor of IEEE Transactions on Systems, Man and Cybernetics: Part B Cybernetics (2011-2013). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR). Dr. Tan has also served as the leading Guest Editors for several famous referred Journals including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, natural computing, Neurocomputing, etc.

Dr. Tan has more than twenty research projects including NSFC, 973 programs, 863 project, etc. His research achievements are summarized as follows:

1) Fireworks algorithm: He proposed FireWorks Algorithm (FWA) in 2010, as an intelligent optimization algorithm, inspired by life firework’s explosion. In the FWA, a novel explosive search manner is well established, which caused a great impact on the intelligent optimization methods. Subsequently, Prof. Tan leaded this team to study FWA deeply. Not only theoretic analysis was completed but also a number of FWA variants were successively proposed and analyzed in detail, including Enhanced FWA, dynFWA, Adaptive FWA, Guided FWA, etc. All of these contributions make the FWA as a promising intelligent optimization algorithm over the famous PSO, ACO, GA, etc. His seminal paper on FWA has been cited 215 times so far.

2) Swarm intelligence: He studied the cooperative mechanisms in swarm intelligence optimization method and swarm robotics systematically, and proposed a number of efficient swarm intelligence optimization algorithms such as clonal particle swarm optimization (CPSO), black-hole PSO, amplifier PSO, etc. They borrowed ideas from different natural phenomena and rules to swarm intelligence optimization, and then developed many new algorithms with better performances. Furthermore, in order to use the inherent parallelism in PSO/FWA based on cheap commercial graphics processing units (GPUs) fully, he proposed an efficient implementation of PSO/FWA based on CUDA, which was the first attempt in this regard, and become the must-cited work in the research of GPU-based swarm intelligence computing.

3) Machine learning techniques: Dr. Tan proposed an intelligent technique based on radial basis function (RBF) network for nonlinear blind source separation (NBSS). In this work, he utilized a RBF network to approximate the inverse of the nonlinear mixing mapping by defining a contrast function consisting of the mutual information and cumulants of the outputs of separating system. The minimization of the contrast function resulted in diminishing the indeterminacies caused by nonlinearity. Further, he proposed a novel approach for training support vector machines (SVMs) efficiently, where a novel optimization criterion was developed to design SVMs by minimizing the upper bound of the VC dimension for a superior generalization capability against other methods. In addition, he proposed many novel techniques such as immune concentration, immune cooperation mechanism and generative adversary networks for malware feature extraction and malware detection.