Prof. Jin Wang
Department of Mathematics and Computer Science, Valdosta State University, Valdosta, GA 31698, USA
Jin Wang is a Professor
of Operations Research in the Department of
Mathematics at Valdosta State University, USA. He
received his Ph.D. degree from the School of
Industrial Engineering at Purdue University in 1994.
His research interests include Operations Research,
Stochastic Modeling and Optimization, Supply Chain
Management, Monte Carlo Simulation, Computational
Finance, Portfolio Management, and Applied
Probability and Statistics. Currently, he is working
on Big Data and Data Mining fields. He has more than
28 years collegiate teaching experience in the field
of quantitative methods and statistics at Purdue
University, Florida State University, Auburn
University, and Valdosta State University. Dr. Wang
has been active in professional research activities.
He has authored articles for publication in referred
journals and conference proceedings. He has been
active in INFORMS, IIE, and the Winter Simulation
Conference and invited to give presentations,
organize and chair sessions at national meetings.
He has participated as a principal investigator in
several research projects funded by federal and
industrial agencies, including the National Science
Foundation, General Motors, and the National Science
Foundation of P.R. China. He was invited as a panel
member at the National Science Foundation Workshop.
Dr. Wang also served as a consultant for financial
firms. His analytical Monte Carlo method using a
multivariate mixture of normal distributions to
simulate market data has made a great impact in
education and the finance industry. This algorithm
was selected as a graduate-level research project
topic for many schools, such as, Columbia University
Management Department, Carnegie Mellon University
Economics and Finance Department, Tilburg University
in Holland, Technische Universitaet Munich in
Germany, Imperial College in London. This method was
also implemented in many financial companies, such
as, Zürcher Kantonal Bank, IRQ, Zürich Switzerland,
Klosbachstrasse, Zürich, Switzerland, Norsk
Regnesentral in Norway, Cutler Group, L.P., Altis
Partners (Jersey) Limited, Windham Capital
Speech Title: On Cluster Based Multivariate Outlier Detection in Data Mining
Abstract: In today’s big data age, mining outliers has become more and more important. It has wide applications in many fields, such as, detection of potential terrorist attacks, credit card fraud detection, network intrusion, clinical trials, severe weather prediction, and athlete performance analysis. On the other side, outlier removal also plays an important role in big data cleaning process. In this study, we propose a normal mixture model for the multivariate outlier detection. Due to the large volume and variety of big data, the classical Euclidean distance measure are not working well for mining multivariate outliers. Variables among different dimensions are usually correlated. Different distant measures will be discussed including the well-known Mahalanobis distance. The mixture model parameters are fitted via the EM algorithm. The K-means clustering algorithm is used to provide the initial inputs for the EM algorithm. The optimal k clusters and initial centers issues will be discussed. A nonsingular robust covariance estimation in calculating Mahalanobis distance will be introduce.
Prof. Yanqing Duan
University of Bedfordshire, UK
Yanqing Duan (BSc, MSc, PhD, SFHEA) is a professor of Information Systems. She is also the founder and director of Business and Information Systems (BISC) at the Business School, University of Bedfordshire. Her principal research interest is the use of the emerging Information and Communication Technologies (ICT) in organisations and their impact on decision making, innovation, and knowledge management. Her recent research has focused on how, why and to what extent Business Analytics is impacting on decision making, innovation, and organisational performance. She has co-ordinated many research projects funded by various funding sources, such as: European Commission, UK Department For International Development (DFID), JISC, British Council, etc. She has published over 180 peer reviewed articles, including papers in European Journal of Information Systems, IEEE transaction on Engineering Management, Information & Management, European Journal of Marketing, Journal of Business Research, The Information Society, Expert Systems with Applications, Information Technology & People.
Understanding the Use and Impact of Big Data and
Analytics: Research Challenges and Opportunities
Abstract: The emergence of Big Data and the advance of analytical technologies provide organisations with extraordinary opportunities to differentiate themselves through analytics. How, why and to what extent is Big Data and Analytics impacting on business operations and performance? Researchers are facing many emerging research challenges as well as opportunities in order to address these questions and to help organisations maximise the value of analytics in the era of Big Data. This presentation will first provide a brief review of the current development of research on understanding of the use and impact of Big Data Analytics. Based on the speaker’s involvement in the research projects focusing on examining the impact of analytics on business competitive advantages, innovation, strategic decision making, and student experience management in UK HEIs, the presentation will discuss the practical implications for industry and the key challenges and opportunities for research emerged from the recent research projects.
Assoc. Prof. Yang Gao
School of Economics and Management, Beijing University of Technology, China
Yang Gao is an Associate Professor of Applied Economics in School of Economics and Management at Beijing University of Technology, China. She received her Ph.D. degree at Guanghua School of Management, Peking University in 2015. Her research interests include Market Microstructure, and Financial Structure. Currently, She is working on the optimization of financial industry structure. Dr. Gao has published more than 10 papers in referred journals and conference proceedings. She has been principal investigator in several research projects funded by National Science Foundation of China, China Postdoctoral Science Foundation.
Speech Title: Impacts of introducing index futures on stock market volatilities: New evidences from China
Abstract: Two index futures, the SSE 50 futures (IH) and the CSI 300 futures (IC), respectively underlying big blue chips and medium-small stocks indexes, were launched on the same day of Apr 2015 in China. However, due to the stock market crash they were strictly restrained four months later. This paper examines how the introductions of IH and IC affect their corresponding stock volatilities, by adopting Hsiao et al. (2012)'s panel data evaluation approach. Results show that IH significantly reduces the spot volatility before (after) the crash, but its function is significantly weakened during the crash. For IC, it always fails to stabilize the spot market and even largely magnifies the volatility during (after) the crash. Further studies give evidence that such different intervention effects on the two markets might be attributed to the different characteristics of their constituent stocks, in particular their different levels of speculative activities.
Assoc. Prof. Chao Wang
Economics and Management, Beijing University
of Technology, China
Chao Wang is an Associate Professor of
Management Science and Engineering in School
of Economics and Management at Beijing
University of Technology, China. He received
his Ph.D. degree at Beijing Jiaotong
University in 2015. He was a visiting
scholar in Purdue University from 2012-2014
and will be a post-doc in Center for Polymer
Studies at Boston University from 2017-2019.
His research interests include Operations
Research, Supply Chain Management, and
Econophysics. Currently, he is working on
the abnormal fluctuations formation
mechanism of stock market based on
investors’ interaction. Dr. Wang has
published more than 30 papers in referred
journals and conference proceedings and
authored or co-authored one book. He has
been principal investigator in several
research projects funded by National Science
Foundation of China, Beijing Social Science
Speech Title: A self-adaptive bat algorithm for the truck and trailer routing problem
Abstract: The truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems due to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers. In this study, we propose a meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature.
Prof. Haiying Ren
School of Economics and Management, Beijing University of Technology, China
Haiying Ren is an Associate Professor of Management
Science and Engineering in School of Economics and
Management at Beijing University of Technology,
China. He received his Ph.D. degree in Industrial
Engineering at University of South Florida in 2000.
His research interests include Technology and
Innovation Management, Multi-agent Simulation,
Knowledge Management, and Operations Management.
Currently, he is working on the behavioral modeling
of the processes of Inventions with knowledge
network representations. He has 14 years collegiate
teaching experience in the field of operations
research, computer simulation, decision theories and
analytical business methods at Beijing University of
Technology. Dr. Ren has published more than 40
papers in referred journals and conference
proceedings and authored or co-authored two
monographs and one book series. He has been
principal investigator in several research projects
funded by Beijing Municipal agencies and Ministry of
Education, including the Beijing Natural Science
Foundation and Beijing Social Science Foundation.
Dr. Ren won a Third Prize at 2010 Beijing Technology
Advancement Award as a key research team member.
Dr. Ren is an Associate Coordinator of Department of
Management Science and Engineering in School of
Economics and Management at Beijing University of
Technology, China. He is also an active member of
Operations Research Society of China.
Speech Title: Dynamic Micro-Mechanism of Breakthrough Inventions Based on Multilevel Knowledge Networks
Abstract: China has become a major patent nation, but not a strong innovation nation, as indicated by the lack of breakthrough inventions (BI’s). In order to increase the productivity of BI’s, we have to understand their mechanism, especially the dynamic and micro processes on individual and team levels. We tackle this problem by integrating invention-problem specific knowledge, individual knowledge, team knowledge and global knowledge in a multilevel knowledge network. Breakthrough inventions can then be modeled as the dynamic searching, learning and recombining operations of the multilevel knowledge network. We design methods for constructing such multilevel knowledge networks, propose building network models for the mental processes, inventive schemes and thinking methods of breakthrough inventors’, modeling dynamic micro-mechanism of breakthrough inventions and verifying the model by patent analysis and multi-agent simulation.
Zeke R. B. Zhou
Tel : +852-3500-0005 (Hong Kong)