Finance with AI and Fintech Applications.  He was Professor at the Chinese University of Hong Kong, and a cofounder of CUHK spin-off technology company. He was an associate Director and Science Advisor for the Hong Kong R&D Center for Logistics and Supply Chain Management Enabling Technologies. He has also worked as a tenured Associate Professor at the School of Management, University of Texas at Dallas. Professor Yan is a celebrated researcher winning a number of awards and recognitions over the years.  Professor Yan’s main research areas are stochastic models, machine learning and algorithms, risk modeling and analysis, and supply chain management.

He received his BSc. and MSc. from Tsinghua University, both in electrical engineering, and his Ph.D. from the University of Toronto in business.  He is a member of Hong Kong Academy of Finance.

He was an associate professor at Harvard. He holds eight U.S. patents in collaboration with the IBM Thomas J. Watson Research Center at Yorktown Heights. He has managed over 30 government and industrial grants.

online social networks, sustainability of social media, diffusion and use of social computing. He develops and applies computational social science approaches to diffusion of innovations, use and impact of new technologies, and formation and dissolution of social networks.

His research appeared in top-tier journals including Management Science and Mathematical Finance with a focus in AI applied to finance. He is currently working on machine learning approach to financial risk management, term structure modelling in low interest rate environment, incentive analysis in matching and sharing platforms.

leading large R&D teams to develop and deliver commercial-grade software products to worldwide customers. Dr. Wu was the Director, Head of Cloud Computing at Artificial Intelligence and Big Data in ASTRI, where he led various projects to apply Blockchain, algorithm trading, data mining in financial industry. Dr. Wu has 8 US patents in big data and networking, and published papers on various journals, IEEE and ACM conference proceedings. He received his Bachelor, Master, PhD degrees in Computer Science from Sun Yat-Sen University.

Engineering and Computer Sciences at University of California Berkeley. He was with Microsoft Research Redmond and Department of Information Engineering, the Chinese University of Hong Kong, before joining the School of Data Science, The City University of Hong Kong as a Professor in 2019. He also received several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, and ACM Multimedia Best Paper Award in 2012. Prof. Chen’s recent research interests include online optimization and algorithms, machine learning in networked and societal systems, and capitalizing the benefit of data-driven prediction in algorithm/system design.

published in top journals and referred conference proceedings in the fields of operations research and quantitative finance, such as Review of Financial Studies, Journal of Econometrics, Operations Research, Mathematics of Operations Research, Mathematical Finance, Finance and Stochastics, Journal of Economic Dynamics and Control. His research is supported by the scheme of General Research Fund, Hong Kong Research Grant Council. He is the director of the Bachelor of Engineering Program in Financial Technology at CUHK and the director of Master of Science Program in Financial Engineering at CUHK Shenzhen. He received his Ph.D. in operations research from Columbia University, and M.S. and B.S. in probability and statistics from Peking University, Beijing.

public opinion, adolescent behavior, and new technology.

Electronic Engineering from Tsinghua University in 2011. Before joining CityU, she was a post-doc in Electrical Engineering at Stanford University. Her research interests are process data analytics, high-dimensional time series modelling, statistical machine learning, smart manufacturing and new material design.

Business Data Analytics (Quantitative Analysis for Business stream). His research work has been published or accepted at the Journal of Finance and Journal of Econometrics. His research work has received major academic and practitioner awards, including the 2018 AQR Insight Award, 2nd Prize of the 2019 Crowell Prize, Unigestion Alternative Risk Premia Research Grant Award, INQUIRE Europe Research Grant Award, and PwC 3535 Finance Forum Annual Best Paper Award. Also, Feng has been invited to present at major academic conferences (AFA, CICF, Informs, and SoFie) and practitioner conferences (CQAsia, Wolfe Research, and INQUIRE Europe). He obtained his PhD and MBA degrees from the University of Chicago in 2017. His research interests include financial time series, empirical asset pricing, machine learning, and quantitative finance.

and his DSc (1991) from Tokyo University. His research interests include wireless networking, sensor networks, distributed systems and Cloud computing, Internet and web technologies.

Finance. His current research primarily focuses on IT business value, business analytics and information security. His teaching objective is to bring his research into class. He is interested in teaching business strategies and introducing new information technologies to enhance corporate competitive advantages. His works have been published in various leading IS journals and international conference proceedings. He currently serves as Associate Editor for Decision Support Systems and the Communications of the AIS.

before joining City University of Hong Kong. His main research interests include credit risk modelling, volatility modelling, and asset pricing.

MISQ, ISR, JMIS, INFORMS JOC, DSS, I&M, JASIST, IJEC, IEEE/ACM Transactions, Nature Nanotechnology, Bioinformatics, among others. He has received about 20 external and internal grants as PI and Co-I. He is a senior member of IEEE, and ACM, and a member of INFORMS and AIS.

College of Business, City University of Hong Kong. He received a bachelor’s degree in Computer Science from Tsinghua University and his Ph.D. in Industrial Engineering and Engineering Management from Hong Kong University of Science and Technology. His research interests include (1) Decision analytics for marketing, logistics, and supply chain; and 2) Interface research between Operations and Marketing, such as pricing, assortment planning, channel management, and advertising. His papers have appeared in journals such as Marketing Science, Manufacturing & Service Operations Management, Operations Research, and Production and Operations Management. Prof Li has provided consulting services to firms in Hong Kong and mainland China and is currently working on data-driven projects spanning marketing, logistics, retailing, and health care operations.

Technology. His research interests include financial engineering, risk management, stochastic simulation, machine learning and business analytics.

University. Before joining CityU, he was an associate professor and the head of the Computer Engineering Department at Southeast University, Nanjing, China. He is also affiliated with Prof. Jonathan Zhu’s Web Mining Lab, which is housed inside the Department of Media and Communication.

degree from University of Maryland at College Park in 1992. He is currently Chair Professor of Data Science at the City University of Hong Kong. His research interests include data analytics, machine learning, latent variable methods; high-dimensional time series latent variable modelling, process monitoring and fault diagnosis, model predictive control, system identification, semiconductor manufacturing control, and data-driven control and optimization. He has over 400 publications in international journals, book chapters, conference papers, and conference presentations with peer-reviewed abstracts. He delivered over 50 invited plenary or keynote speeches and over 120 invited technical seminars worldwide. He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He served as Senior Editor of Journal of Process Control, Editor of Control Engineering Practice, Member of the Editorial Board for Journal of Chemometrics, and Associate Editor for several other journals.

computation outsourcing security in the context of cloud computing, blockchain and decentralized application, network security in emerging Internet architecture, multimedia security, and privacy-enhancing technologies in the context of big data and IoT. His research has been supported by multiple government research fund agencies, including National Natural Science Foundation of China, Hong Kong Research Grants Council, and Hong Kong Innovation and Technology Commission, as well as Amazon Web Services, and Microsoft Azure.

academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

Management from China Academia Sinica, and Ph.D. in Finance from Syracuse University, USA. Before joining the City University of Hong Kong in 2005, he has taught at Syracuse University. His main research interests include fixed income securities, asset pricing, corporate finance, and market microstructure.

Business School. His main research fields are the operations-finance interface, economic networks, quantitative investment, and machine learning applications. Prior to joining CUHK, he served as an Assistant Professor in the Department of Management Sciences at College of Business, City University of Hong Kong (2016-2019). He also worked as a Quantitative Strategist at Deutsche Bank New York (2015-2016). He received his bachelor degree in Electronic Engineering from Tsinghua and both his MBA and Ph.D. (major in operations, minor in economics & finance) from Chicago Booth.

the College of Business. His areas of research and teaching include corporate finance, mergers & acquisitions, financial accounting, and capital markets. He is a CFA charterholder. Prof. Yu is on board of directors for a number of listed companies in China and Hong Kong.

This includes topics on optimal design of inventory-production systems, incentive issues in supply chains and pricing strategies. His research work has been published in leading business journals including Manufacturing & Service Operations Management, Marketing Science, Operations Research, and Production and Operations Management.

Management Information Systems from Purdue University. Prior to joining City University of Hong Kong, he was a faculty member at the University of Texas, Dallas. His research interests focus on the economic and operational aspects of information security and information systems. His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Journal of Management Information Systems, Decision Support Systems, and other journals.

degree in Systems and Industrial Engineering with a minor in Management Information Systems from The University of Arizona, in 2011 and 2012, respectively. His research interests include healthcare data analytics, medical informatics, network science, social computing, data mining, machine learning, and semantic web.

degree in Systems Engineering and Engineering Management from the Chinese University of Hong Kong, Hong Kong, China, in 2008. His research focuses on data mining and computational intelligence with applications in modeling, monitoring, optimization and operations of systems in the renewable energy, energy saving, and intelligent transportation.

University. His research interests include deep learning theory, deep neural networks, and applications of machine learning. 

of Hong Kong, he was a research staff at Beidu AI Group. He received his bachelor degree in applied math from UC Berkeley and Ph.D. degree in statistics from UCLA. His current research interest includes random tensor theory, minimax theory of PCA and experimental design on network data. He is also interested in information retrieval, neural ranking and generative adversarial network.

member of Columbia’s Data Science Institute. Garud Iyengar’s research is focused on understanding uncertain systems and exploiting available information using data-driven control and optimization algorithms.  He and his students have explored applications in many diverse fields, such as machine learning, systemic risk, asset management, and operations management.

of Texas, Austin. His research areas include spatial data analysis and visualization, time series analysis and forecasting, Bayes Networks for process modeling and decision making.

in 2017. He is a recipient of the NSF CAREER Award in 2017 and serves on the editorial boards of Operations Research and INFORMS Journal on Computing. His work focuses on the quantification and mitigation of statistical errors and risks in integrating these methodologies with data, and the design and analysis of efficient computation and solution machineries. Henry is also broadly interested in the intersecting domains of computational probability, stochastic and robust optimization, and machine learning.

the intersection of communication, information, and technology, echoing his education background, an intersection of arts and technology. His research focuses on Consumer Health Informatics, Social Networks, Human-Computer Interaction, and Social Media.

at University of Wisconsin at Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology, and financial engineering. He has over 100 scientific publications in applied mathematics, computer science, electrical engineering, financial econometrics, medical information, optimization, and statistics, among others. 

cumulative prospect theory, and in general time inconsistent problems. He directs the FDT Center for Intelligent Asset Management, a research center funded by a FinTech company. He has addressed the 2010 International Congress of Mathematicians and has been awarded the Wolfson Research Award from The Royal Society (UK), the Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, the Humboldt Distinguished Lecturer, and the Alexander von Humboldt Research Fellowship. He is both an IEEE Fellow and a SIAM Fellow. Prof. Zhou received his Ph.D. in Operations Research and Control Theory from Fudan University in China in 1989. He was the Nomura Professor of Mathematical Finance and the Director of Nomura Center for Mathematical Finance at University of Oxford during 2007-2016 before joining Columbia.

and probabilistic ranking models. Continuous-time stochastic processes, arising as the limit of discrete algorithms and large particle systems, provide feasible analysis and unique insight into real-world problems of machine learning and finance. Ranking models serve as fundamental tools to understand various social phenomena such as elections and recommendation mechanism. Tang’s current research interest is to improve the efficiency of machine learning algorithms using stochastic tools, and to develop robust AI methodology for the emerging fintech market. Examples include random graph modeling and queueing analysis of blockchain protocols, dynamic portfolio selection via repulsive point processes, and high-dimensional continuous optimization problems. Tang was a postdoctoral researcher in the Department of IEOR at UC Berkeley from 2019-2020, and an assistant adjunct professor in the Department of Mathematics at UCLA from 2017-2019. He received his PhD in Statistics from UC Berkeley in 2017 and his engineering degree at Ecole Polytechnique in 2013. He received the Prize for Excellence in Financial Markets from Morgan Stanley in 2017.

articles in areas such as finance, stochastic processes and estimation, operations management and inventory control. 

frequency analysis and empirical asset pricing. Her current work focuses on developing machine learning methodologies that are adapted to financial and economical data structures in the era of complex and ample data.

numerous awards, including the prestigious Fields Medal in 1994, the Grand Prize Ampère from the French Academy of Sciences, the Grand Prize from the INRIA, France, the IBM Prize, and the Philip Morris Prize.

his Ph.D. from the Rutgers Business School at Rutgers University. Before joining Rutgers, he received his M.S. degree in Physics from Rutgers University and a B.S. degree from the School of the Gifted Young at the University of Science and Technology of China (USTC). His general areas of research are data mining, supply chain analytics, urban computing, and large-scale optimization, with a focus on developing effective and efficient data mining techniques for emerging big data & supply chain applications. His ongoing research projects include intelligent transportation and logistics services, data-driven operations and supply chain management, supply chain network design and optimization, multi & omnichannel business. He has published prolifically in top venues of knowledge discovery and data mining.

Economics and a Bachelor of Science in Engineering from the University of Pennsylvania’s Wharton School and School of Engineering and Applied Sciences. He is a financial economist and conduct research on financial markets. His research interests include asset pricing, financial econometrics, investments, commodities, and return predictability, and my work has been covered by Forbes and Institutional Investor. He is on the editorial board of the Journal of Portfolio Management and the Global Commodities Applied Research Digest.

decision and robust portfolio management.