Liao, Ming（廖明）

Biography

Professor Ming Liao has over two decades of practical experience in creating comprehensive AI and digital transformation solutions across diverse industries, including Technology, Consumer Goods, Retail, Luxury, Automotive, Pharmaceuticals, and Financial Services. He has a distinguished track record of delivering high-quality solutions that have produced significant business impact. He has held senior management positions in leading multinational companies, such as Boston Consulting Group (BCG), Meta, Huawei, Unilever etc., across the United States, Singapore, and China.

Currently, Professor Liao serves as a Professor of Practice in Business Analytics within the Department of Decisions, Operations, and Technology at CUHK Business School. His research and teaching interests are Generative AI, Data Analytics, Digital Transformation, Marketing, Fintech, and ESG. In addition, he is the Director of Research at the Asia-Pacific Institute of Business. Professor Liao has been leading and designing executive education programs and has collaborated on applied research projects with various corporations.

Before joining CUHK, Professor Liao was a Partner, Vice President of Data Science at Boston Consulting Group (BCG), where he developed Generative AI/data strategies, evaluated and prioritized use cases, and led the end-to-end AI/ML project implementation to empower organizations to leverage AI-driven digital transformation to achieve competitive growth and drive exceptional results. He was also an adjunct Professor at New York University (NYU) and China Europe International Business School (CEIBS). Ming holds a PhD from Duke University in the USA, where his research focused on Bayesian Machine Learning.

Teaching Areas

Generative AI
Machine Learning
Digital Transformation
Marketing
ESG

Lin, Yunduan（林韵端）

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Biography

Professor Yunduan Lin joined the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School as an Assistant Professor in August 2024. Professor Lin received her PhD and MS degrees from the University of California, Berkeley and her BEng from Tsinghua University.

Her research centres on societal operations management, specifically on social network analytics and platform operations. She aims to create a more precise reflection of the interconnected societal systems, which, in turn, enables improved decision-making for business problems.

Research Interests

Social Network Analytics
Platform Operations
Supply Chain Management
AI and Machine Learning in Business

Publications & Working Papers
Journal
- Yunduan Lin, Heng Zhang, Renyu Zhang, and Zuo-Jun Max Shen (2024). “Nonprogressive diffusion on social networks: Approximation and applications,” under revision.
- Yunduan Lin, Mengxin Wang, Heng Zhang, Renyu Zhang, and Zuo-Jun Max Shen (2024), “Content Promotion for Online Content Platforms with the Diffusion Effect,” Manufacturing & Service Operations Management, 26(3), 1062-1081.
- Yunduan Lin, and Ruimin Li (2020), “Real-time Traffic Accidents Post-impact Prediction: Based on Crowdsourcing Data,” Accident Analysis & Prevention, 145, 105696.
Refereed Conference Proceedings
- Haoting Zhang, Donglin Zhan., Yunduan Lin, Jinghai He, Qing Zhu, Zuo-Jun Max Shen, and Zeyu Zheng (2024), “Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data,” Proceedings of the Fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research, 248, 39-54.
- Yifan Zheng, Yoonsuk Huh, Qianqian Su, Jiaming Wang, Yunduan Lin, Kai Vetter, and Youngho Seo (2020), “Collimatorless scintigraphy for imaging extremely low activity targeted alpha therapy (TAT) with weighted robust least squares (WRLs),” In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 803-811. Cham: Springer International Publishing.

Grants
- CUHK Improvement on Competitiveness in Hiring New Faculties Funding Scheme, PI, with HKD 1,307,270, 2024 (PI)

Awards & Honours
- Outstanding Graduate Student Instructor Award, University of California, Berkeley, 2024
- Finalist, the 18th INFORMS DMDA Workshop Best Paper Competition Award (Theoretical Track), 2023
- Finalist, INFORMS Minority Issues Forum Poster Competition, 2023
- First-Place Prize, the 13th POMS-HK International Conference Best Student Paper Award, 2023
- Second Place, OR/MS Tomorrow Mini-poster Competition, 2022
- Winner, INFORMS Social Media Analytics Best Student Paper Award, 2022

Zhang, Dongcheng（張東成）

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Biography

Professor Zhang is an Assistant Professor at the Department of Decisions, Operations and Technology in the Chinese University of Hong Kong (CUHK) Business School. Prior to joining CUHK, he was a post-doctoral fellow at the Goizueta Business School of Emory University. He received his Ph.D. in Management Science and Engineering, BE in Engineering, and BA in Management from Tsinghua University. His research focuses on developing and applying machine learning algorithms, statistical methods, and analytical models to improve decision-making in digital marketing and management information systems. In particular, he is interested in developing interpretable and theory-driven machine learning/deep learning algorithms for substantive business problems (e.g., text mining, consumer choices, and causal inference).

Teaching Areas

AI in Business
Data Analytics
Machine Learning/Deep Learning
Probabilistic Graphical Models

Research Interests

Digital Marketing
Online Communities
Platform Strategy
Causal Inference

Publications & Working Papers
- Dongcheng Zhang, Kunpeng Zhang, Yi Yang, and David Schweidel, “TM-OKC: An Unsupervised Topic Model for Text in Online Knowledge Communities,” Management Information Systems Quarterly, accepted.
- Dongcheng Zhang, Hanchen Jiang, Maoshan Qiang, Kunpeng Zhang, and Liangfei Qiu, “Time to Stop? An Empirical Investigation on the Consequences of Canceling Monetary Incentives on a Digital Platform,” Information Systems Research, accepted.

Law, Kody（羅其龍）

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Biography

Professor Kody Law is an Assistant Professor of Finance at the Chinese University of Hong Kong (CUHK) Business School. He received Ph.D. in finance from the Cornell, an MS in Business Analytics from the University of California, San Diego and BBA from the University of California, Berkeley. His research interests include fintech, financial intermediation, real estate, and private equity.

Teaching Areas

Machine Learning
Data Analytics

Research Interests

Fintech
Financial Intermediation
Private Equity
Mortgages
Real Estate
Entrepreneurship

Publications & Working Papers
- Kody Law (2024), “Fintech and Minority Welfare: Evidence from the Mortgage Market,” Working Paper.
- Kody Law (2023), “Private Equity and Local Entrepreneurship – Spillover Effects of Institutional Ownership of Single Family Homes,” Working Paper.
- Kody Law (2023), “The Opioid Epidemic and Mortgage Lending: Credit or Demand Shock?,” Working Paper.
- Kody Law and Nathan Mislang (2022), “The Heterogeneity of Bank Responses to the Fintech Challenge,” Working Paper.

Lee, Myunghwan（李明煥）

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Biography

Professor Lee is an Assistant Professor from the Department of Decisions, Operations and Technology (DOT) at the Chinese University of Hong Kong (CUHK) Business School. His research seeks to understand the opportunities and challenges regarding AI in the business innovation context. Before joining CUHK, he received his Ph.D. from the Sauder School of Business at the University of British Columbia in Vancouver and his B.A. and M.S. from Yonsei University in Seoul.

Teaching Areas

Business Information Systems

Research Interests

Economics of AI
AI Strategies
Managing AI
Mobile Device Management

Publications & Working Papers
- M. Lee, G. M. Lee, H. Cavusoglu, and M. D. L. Seidel, “Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct,” Working Paper.
- M. Lee, G. M. Lee, D. H. Shin, and S. P. Han, “Robots Serve Humans: Understanding the Economic and Societal Impacts of AI Robots in the Service Industry,” Working Paper.
- J. Park, M. Lee, and G. M. Lee, “Mobile Resilience: The Effect of Mobile Device Management on Firm Performance during the COVID-19 Pandemic,” Working Paper.
- M. Lee, T. Sturm, and G. M. Lee, “Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations,” Working Paper.

Grants
- “R&D Collaboration Exploration for AI-Based Personalized Healthcare Solution”, Research Grant awarded by K-TAG (Korea Technology Advisory Group) with USD $48,663, 2023 (Co-investigator)
- “AI and Analytics Frameworks for Global Inter-Firm Value Network Construction”, Research Grant awarded by KISTI (Korea Institute of Science and Technology Information) with USD $80,000, 2018-2022 (Co-investigator)

Awards & Honours
- Teaching Award (Paul Chwelos Memorial Graduate Scholarship), Sauder School of Business, The University of British Columbia, 2024
- UBC President’s Academic Excellence Initiative PhD Award, Sauder School of Business, The University of British Columbia, 2020-2023
- UBC International Tuition Award, Sauder School of Business, The University of British Columbia, 2020-2023

Academic/Professional Services
- Reviewer, Production and Operations Management (POMS), 2024
- Program Committee Member, INFORMS Workshop on Data Science, 2024
- Program Committee Member, KrAIS Summer Workshop, 2023

Dong, Wendy Wanxue（董婉雪）

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Biography

Professor Dong is an Assistant Professor in the Department of Decisions, Operations and Technology at the CUHK Business School. She hold a PhD in Information Systems from the McCombs School of Business, the University of Texas at Austin. Her research aims to develop machine learning and artificial intelligence algorithms to solve business problems. Her recent projects develop novel ML and AI methods to evaluate workers’ decision accuracy and societal bias in many key domains including health care, fraud detection, fact checking, finance, and online labor markets. She earned a dual B.S. degree in Computer Science and Statistics from Purdue University, IN.

Teaching Areas

Business Information Systems

Research Interests

Machine Learning
Predictive Modeling
Experts/Labelers’ Decision Quality Assessment
Human AI Collaboration
Fair and Responsible AI

Publications & Working Papers
- Wanxue Dong, Maytal Saar-Tsechansky, and Tomer Geva (2024), “A Machine Learning Framework for Assessing Experts’ Decision Quality,” Management Science, Articles in Advance.
- Wanxue Dong, Maria De Arteaga, and Maytal Saar-Tsechansky, “A Machine Learning-Based Framework towards Assessment of Decision-Makers’ Biases,” under review at MIS Quarterly.

Grants
- Provost Research Grant awarded by The University of Texas at Austin, 2021-2022

Awards & Honours
- Graduate School Summer, The University of Texas at Austin, 2022
- College Continuing Fellowship, The University of Texas at Austin, 2021
- McCombs Dean’s Fellowship, The University of Texas at Austin, 2017-2021

Academic/Professional Services
- Reviewer, Management Information Systems Quarterly (MISQ), 2023
- Reviewer, International Conference on Information Systems (ICIS), 2023

Whelan, Paul

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Biography

Professor Paul Whelan is an Associate Professor in the Department of Finance. Before joining CUHK in 2023, he was an Associate Professor of Finance at the Copenhagen Business School (CBS). He received his PhD degree in Financial Economics from Imperial College London.

Prof. Whelan’s papers have been published in the Journal of Finance, Journal of Financial Economics, the Review of Financial Studies, and Management Science, and has won several awards for his research.

Teaching Areas

Financial Economics
Financial Mathematics
Asset Pricing
Derivative Markets

Research Interests

Prof. Whelan’s research focuses on two distinct subfields:

Asset pricing and belief formation
Market microstructure

Publications & Working Papers
Published Papers
- Paul Whelan, Ingomar Krohn and Philippe Mueller, “Foreign Exchange Returns and Fixings Around the Clock,” Journal of Finance, forthcoming.
- Paul Whelan, Nina Boyarchenko and Lars Larsen (2022), “The Overnight Drift,” Review of Financial Studies.
- Paul Whelan, Andrea Buraschi and Ilaria Piatti (2021), “Subjective Bond Returns and Belief Aggregation,” Review of Financial Studies.
- Paul Whelan, Matteo Leombroni, Andrea Vedolin and Gyuri Venter (2021), “Central Bank Communication and the Yield Curve,” Journal of Financial Economics.
- Paul Whelan and Andrea Buraschi (2020), “Speculation, Sentiment, and Interest Rates,” Management Science.
- Paul Whelan and Andrea Buraschi (2016), “Bond Markets and Conventional Monetary Policy,” ‘Handbook of Fixed Income’ edited by Pietro Veronesi.
- Paul Whelan and Andrea Buraschi (2016), Bond Markets and Unconventional Monetary Policy,” ‘Handbook of Fixed Income’ edited by Pietro Veronesi.
Working Papers
- Paul Whelan, Daniel Pesch and Ilaria Piatti, “Subjective Risk Premia in Bond and FX Markets”
- Paul Whelan and Carsten Sørensen, “Money as Options”
- Paul Whelan, Nina Boyarchenko, Lars Larsen and Gyuri Venter, “Demand Shock Asymmetry”

Awards & Grants
- INQUIRE Europe Grant: Central Bank Shocks, 2022.
- Canadian Derivatives Institute Annual Research Grant: Foreign Exchange Returns and Fixings Around the Clocks
- Danish Research Council DFF-grant 1: principle early stage researcher, 2018.
- Danish Research Council DFF-grant 1: early stage researcher joint with Gyuri Venter, 2018.
- SFS Asian Cavalcade Beijing Best Paper Award: Subjective Bond Returns and Belief Aggrega-tion, 2017.
- CICF Xiamen Best Paper Award: Subjective Bond Returns and Belief Aggregation, 2016.
- Best PhD Thesis Imperial College London, 2015.
- GARP Risk Management Research Award, 2013.
- Winner Q-Group Grant Award: Speculation, Sentiment, and Interest Rates, 2012.
- AFA Doctoral Student Travel Grant, 2012.

Chen, Zhi（陳植）

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Biography

Prof. Zhi Chen is an Assistant Professor in the Department of Decisions, Operations and Technology, CUHK Business School, The Chinese University of Hong Kong. He obtained his PhD from NUS Business School, National University of Singapore and his BE from Tsinghua University, China.

His primary research involves developing models and designing algorithms for analytics under uncertainty with different levels of data availability as well as applications in business, economics, finance, and operations. He is also interested in how to compete or cooperate in joint activities such as resource allocation and risk management. His works appear in journals such as INFORMS Journal on Computing, Management Science, Operations Research, Production and Operations Management, Mathematical Finance, Transportation Science, and TRB.

Teaching Areas

Business Analytics
Data Mining & Machine Learning
Operations & Supply Chain Management
Risk Management

Research Interests

Analytics under Uncertainty
Data-Driven Analytics
Risk Management
Robust Optimisation

Publications & Working Papers
- Jie Hu, Zhi Chen, and Shuming Wang (2024), “Budget-Driven Multi-Period Hub Location: A Time Series Approach,” Operations Research, forthcoming.
- Zhi Chen, Zhenyu Hu, and Ruiqin Wang (2024), “Screening with Limited Information: A Dual Perspective,” Operations Research, 72(4), 1487–1504.
- Yue Zhao, Zhi Chen, and Zhenzhen Zhang (2023), “Distributionally Robust Chance Constrained p-Hub Center Problem,” INFORMS Journal on Computing, 35(6), 1361-1382.
- Feng Liu, Zhi Chen, and Shuming Wang (2023), “Globalized Distributionally Robust Counterpart,” INFORMS Journal on Computing, 35(5), 1120-1142.
- Zhi Chen, Daniel Kuhn, and Wolfram Wiesemann (2023), “On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls,” Operations Research Letters, 51(3), 226-233.
- Haolin Ruan, Zhi Chen, and Chin Pang Ho (2022), “Adjustable Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets,” INFORMS Journal on Computing, 35(5), 1002-1023.
- Zhi Chen and Peng Xiong (2022), “RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy,” INFORMS Journal on Computing, 35(4), 717-724.
- Guomin Xing, Zhi Chen, Yuanguang Zhong, and Yong-Wu Zhou (2022), “Mitigating Supply Risk with Limited Information: Emergency Supply and Responsive Pricing,” Production and Operations Management, forthcoming.
- Zhi Chen, Daniel Kuhn, and Wolfram Wiesemann (2022), “Data-Driven Chance Constrained Programs over Wasserstein Balls,” Operations Research, 72(1): 410–424.
- Yue Zhao, Zhi Chen, Andrew Lim, and Zhenzhen Zhang (2022), “Vessel Deployment with Limited Information: Distributionally Robust Chance Constrained Models,” Transportation Research Part B: Methodological, 161, 197-217.
- Zhi Chen and Weijun Xie (2021), “Regret in the Newsvendor Model with Demand and Yield Randomness,” Production and Operations Management, 30(11), 4176-4197.
- Zhi Chen and Weijun Xie (2021), “Sharing the Value-at-Risk under Distributional Ambiguity,” Mathematical Finance, 31(1), 531-559.
- Zhi Chen, Zhenyu Hu, and Qinshen Tang (2020), “Allocation Inequality in Cost Sharing Problem,” Journal of Mathematical Economics, 91, 111-120.
- Zhi Chen, Melvyn Sim, and Peng Xiong (2020), “Robust Stochastic Optimization Made Easy with RSOME,” Management Science, 66(8), 3329-3339.
- Shuming Wang, Zhi Chen, and Tianqi Liu (2020), “Distributionally Robust Hub Location,” Transportation Science, 54(5), 1189-1210.
- Zhi Chen, Melvyn Sim, and Huan Xu (2019), “Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets,” Operations Research, 67(5), 1328-1344.

Grants
- “Theory and Applications of Robust Optimization” Excellent Young Scientists Fund awarded by National Natural Science Foundation of China with RMB 2,000,000, 2025-2027 (Principal Investigator)
- “A Distribution-Free Model of Time Series”, General Research Fund awarded by Hong Kong Research Grants Council with HK$414,059, 2024-2025 (Principal Investigator)
- CUHK “Improvement on Competitiveness in Hiring New Faculties” Funding Scheme awarded by The Chinese University of Hong Kong with HK$1,500,000 (Principal Investigator)
- “Robust Mechanism Design with Limited Information”, General Research Fund awarded by Hong Kong Research Grants Council with HK$925,120, 2023-2025 (Principal Investigator)
- “Financial Systemic Risk Measures based on Monte Carlo Simulation: Theory and Methods”, NSFC/RGC Joint Research Scheme awarded by National Natural Science Foundation of China & Hong Kong Research Grants Council, 2022-2025 (Co-Investigator)
- “A Python Algebraic Modeling Package for Robust Optimization”, Strategic Research Grant awarded by City University of Hong Kong with HK$100,000, 2021-2023 (Principal Investigator)
- “The Hurwicz Criterion for Data-Driven Decision-Making under Uncertainty”, Early Career Scheme awarded by Hong Kong Research Grants Council, HK$628,950, 2020-2023 (Principal Investigator)
- “Risk Sharing under Distributional Ambiguity: Stability, Inequality, and Monotonicity”, Start-Up Grant awarded by City University of Hong Kong, with HK$300,000, 2019-2022 (Principal Investigator)

Awards & Honours
- College Research Excellence Award, College of Business, City University of Hong Kong, 2022
- Finalist, George E. Nicholson Student Paper Competition, INFORMS, 2017

Academic/Professional Services
- Reviewer for INFORMS Journal on Computing, Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management, Mathematics of Operations Research, Mathematical Programmin, Naval Research Logistics, Operations Research Letters, SIAM Journal on Optimization, TRB, TRE, Transportation Science, ICLR, ICML, and NeurIPS.

Chen, Jun（陳軍）

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Biography

Prof. Jun Chen is an Assistant Professor at the CUHK Business School. His research interests lie in corporate finance, financial accounting, and household finance. His latest projects have focused on the real effects of information in financial markets. He received his PhD in Finance and MFin degrees from Rady School of Management of the University of California San Diego, and BS in Mathematics from Peking University.

Teaching Areas

Data Analytics
Machine Learning

Research Interests

Corporate Finance
Financial Accounting
Household Finance

Karhade, Prasanna

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Biography

Prof. Prasanna Karhade joined the CUHK Business School as an Associate Professor in the Department of Decisions, Operations and Technology in August 2022.

Prof. Karhade is interested in digital innovation, design thinking and entrepreneurship. His research has been published in MIS Quarterly, Information Systems Research, and Journal of Management Information Systems.

He has currently started an ambitious research programme on micro-financing in GREAT (growing, rural, eastern, aspirational, and transitional) economies. Prof. Karhade loves hiking, poetry & meditation. Prasanna means happiness and so there is no incorrect way to pronounce Prasanna — as long as you do it with a smile.

Teaching Areas

Digital Innovation
Entrepreneurship
FinTech

Research Interests

Digital Innovation & Entrepreneurship
Digital Platforms & Decolonization
Governance & Control of IT in Family Business

Publications and Working Papers
Governance & Control
- Konsynski, B.R., Kathuria, A., and Karhade, P. (2024), “Cognitive Reapportionment and the Art of Letting Go: Theoretical Framework for the Allocation of Decision Rights,” Journal of Management Information Systems, 41(2), 328-340. [Authors contributed equally; names in reverse alphabetical order.]
- Kathuria, A., Karhade, P., Ning, X., and Konsynski, B. (2023), “Blood and Water: IT Investment and Control in Family-Owned Businesses,” Journal of Management Information Systems, 40(1), 208-238.
- Karhade, P., Shaw, M.J., and Subramanyam, R. (2015), “Patterns in Information Systems Portfolio Prioritization: Evidence from Decision Tree Induction,” MIS Quarterly, 39(2), 413-433.
- Susarla, A., Subramanyam, R., and Karhade, P. (2011), “Contractual Provisions to Mitigate Holdup: Evidence from Information Technology Outsourcing,” Information Systems Research, 21(1), 37-55.
Digital Platforms & Decolonization
- Kathuria, A., Karhade, P., and Konsynski, B.R. (2020), “In the realm of hungry ghosts: Multi-level theory for restaurant participation in food delivery platforms,” Journal of Management Information Systems ,37(2), 396-430. [Kathuria A. and Karhade P. are both co-first authors.]
- Karhade, P. and Kathuria, A. (2020), “Missing Impact of Ratings on Platform Participation in India: A Call for Research in GREAT Domains,” Communications of the Association for Information Systems, 47(Article 19), 364-381. [Both authors are co-first authors.]
Digital Innovation & Entrepreneurship
- Karhade, P. and Dong, J.Q. (2021), “IT Investment & Commercialized Innovation Performance: Dynamic Adjustment Costs and Curvilinear Effects,” MIS Quarterly, 45(3), 1007-1024.
- Karhade, P. and Dong, J.Q. (2021), “Innovation outcomes of the digitally enabled collaborative problemistic search capability,” MIS Quarterly, 45(2), 693-718.
- Dong, J.Q., Karhade, P., Rai, A., and Xu, S.X. (2021), “How Firms Make Information Technology Investment Decisions: Toward a Behavioral Agency Theory,” Journal of Management Information Systems, 38(1), 29-58. [Authors listed alphabetically.]

Grants
- “Search for Innovation: Integrating the Behavioural Theory of the Firm with Agency-Theoretic Explanations”, awarded by, Research Grants Council (RGC)of Hong Kong with HK$717,470, 2018-2021
- “IT Assets and Firm Innovation Output: An Empirical Examination”, awarded by Research Grants Council (RGC) of Hong Kong with HK$ 215,670, 2012-2014

Award & Honours
- Excellence in Teaching Award Nomination, University of Hawaii, 2020
- Dean’s Recognition of Excellent Teaching Performance, Hong Kong University of Science and Technology, 2013-14
- Dean’s Recognition of Excellent Teaching Performance, Hong Kong University of Science and Technology, 2014-15
- List of Teachers Ranked as Excellent by Their Students, University of Illinois at Urbana Champaign, 2006
- List of Teachers Ranked as Excellent by Their Students, University of Illinois at Urbana Champaign, 2007

Academic/Professional Services
- Member, Association for Information Systems (AIS)
- Member, Academy of Management (AoM)