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Main Organisers


Chun-houh Chen

Chun-houh Chen, Ph.D., Research Fellow & Director of The Institute of Statistical Science, Academia Sinica, received his Ph.D. in Mathematics (program in statistics) from the University of California, Los Angeles (UCLA) in 1992. Dr. Chun-houh Chen started his professional career as an assistant professor at The George Washington University (Department of Statistics/Computer and Information Systems), USA. In 1993, Dr. Chen went back to Taiwan to continue his research career at the Institute of Statistical Science, Academia Sinica. His current research interests are matrix visualization and information mining for exploratory data analysis. Dr. Chen's group is now working on methodologies and applications of matrix visualization techniques for visualizing different types of data sets from various biomedical studies and social surveys.


Ying Chen

Prof. Ying Chen is a financial statistician and data scientist. She is Associate Professor in Department of Statistics and Applied Probability at the National University of Singapore. She is developing statistical modelling and machine learning methods in complex (nonstationary, high frequency and large dimensional) data analytics and forecasting, text mining and sentiment analysis, network analysis. She works on data of cryptocurrency, limit order book, and renewable energy.


Thorsten Koch

Prof. Dr. Thorsten Koch is Professor for Software and Algorithms for Discrete Optimization at TU-Berlin and director of the Mathematical Optimization Methods and the Scientific Information departments at the Zuse Institute Berlin (ZIB). He has worked in several areas, especially the planning of infrastructure networks, chip verification, mathematical education and integer programming. From 2008-2014 he was the coordinator of the FORNE project, an industry collaboration project regarding gas transportation involving five universities and two research institutes. The project received the 2016 EURO Excellence in Practice Award of the European OR Society. Since 2013 he is head of the GasLab and the SynLab within the Research Campus MODAL (Mathematical Optimization and Data Analysis Laboratory).


Junji Nakano

Prof. Junji Nakano is a professor of department of statistical modeling at the Institute of Statistical Mathematics (ISM), Tokyo, Japan. After graduating from Tokyo university, Japan, he worked at Tokushima University, Saitama University and Hitotsubashi University in Japan. He joined ISM in 1999. His research topics are: time series analysis, statistical software, big data analysis including data visualization. He is a member of the International Association for Statistical Computing (IASC), the International Statistical Institute (ISI), the Japan Statistical Society (JSS), etc.




Tomaso Aste

Tomaso Aste is professor of Complexity Science at UCL Computer Science Department. A trained Physicist, has substantially contributed to research in complex structures analysis, financial systems modelling and machine learning. He is passionate in the investigation of the effect of technologies on society and currently he focuses on the application of Blockchain Technologies to domains beyond digital currencies. He is Scientific Director and Founder of the UCL Centre for Blockchain Technologies; Head and Founder of the Financial Computing and Analytics Group; Programme Director of the MSc in Financial Risk Management; Vice-Director of the Centre for doctoral Training in Financial Computing & Analytics; Member of the Board of the ESRC LSE-UCL Systemic Risk Centre. Prior to UCL he held positions in UK and Australia. He is advising and consulting for financial institutions, banks and digital-economy companies and startups.


Jaeyong Lee

Jaeyong Lee is a Professor in the Department of Statistics at Seoul National University. He received his Ph.D. in statistics from Purdue University in 1998. After graduation, he worked as a post-doctoral fellow at National Institute of Statistical Sciences, U.S.A., and as an Assistant Professor at the Pennsylvania State University. In 2003, he joined the Seoul National University. His research interest covers many aspects of the Bayesian statistics. In particular, his research topics include Bayesian nonparametrics, Bayesian asymptotics, high-dimensional models and differential equation models.


Mike K P So

Mike So is an Associate Professor of the Department of Information Systems, Business Statistics and Operations Management of The Hong Kong University of Science and Technology (HKUST). He devotes to excellence in research on nonlinear time series analysis, dynamic modeling of economic & financial data, Bayesian analysis, risk management and business analytics. He is an Elected Member of the International Statistical Institute. His research findings have been published in more than 60 scholarly articles in international journals. Active in university and industry collaborations, he has served as an advisor in numerous collaborative projects with mutual funds, stock exchange, technology companies, healthcare industry and international companies in financial and business analytics areas. Currently, he is the regional director of the Hong Kong Chapter of Professional Risk Managers’ International Association (PRMIA). He is also the founding director of the Business Analytics Program and the Risk Management and Business Intelligence Program of HKUST.


I-Ping Tu

I-Ping Tu, Ph.D., Research Fellow & Deputy Director of The Institute of Statistical Science, Academia Sinica, received her Ph.D. in Statistics from Stanford University in 1997. In 2003, Dr. Tu went back to Taiwan to continue her research career at the Institute of Statistical Science, Academia Sinica. Her current research mainly focuses on cryo-electron microscopy (cryo-EM) image analysis. In recent years, technical breakthrough has transformed cryo-EM to become a main tool for determination of molecular structure to atomic resolution without crystals or in solution. However, the process of structural determination from single-particle cryo-EM images is still very challenging because it involves processing extremely noisy images. Dr. Tu’s group is working on developing statistical methods to improve the highly noise image analysis.