Haipeng SHEN
Prof. Haipeng SHEN
Innovation and Information Management
Associate Dean (Executive Education)
Patrick S C Poon Professor in Analytics and Innovation
Chair of Business Analytics and Innovation

3917 1624

KK 815

Academic & Professional Qualification
  • PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003
  • MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000
  • BS in Mathematics, School of Mathematical Sciences, Peking University, 1998

Haipeng Shen joined HKU in September 2015 as a Professor of Innovation and Information Management at the Faculty of Business and Economics (now HKU Business School). Before joining HKU, he was a Professor of Statistics and Operations Research, University of North Carolina at Chapel Hill, USA.


Business Analytics, Business Data Analysis, Decision and Risk Analysis, Statistics

Research Interest

Data-driven decision making in the face of uncertainty: big data, business analytics, healthcare analytics, service engineering.

Selected Publications
  • Ningyuan Chen, Ragıp Gürlek, Donald K. K. Lee, H. Shen (2024). Can Customer Arrival Rates Be Modelled by Sine Waves? Service Science, 16(2), 70-84.
  • Zhan, Pan, Law, Shen (2024) Stakeholders’ Knowledge, Attitude and Practice of Adopting Modular Integrated Construction for Sustainable Development in Hong Kong, Journal of Management Engineering, 40.
  • Dai*, Yang, Li, Zhao, Lin, Jiang, Wang, Li, Shen (2024) A Clinically Actionable and Explainable Real-Time Risk Assessment Framework for Stroke-Associated Pneumonia, Artificial Intelligence in Medicine, 149, 102772.
  • Jingxuan Wang, H. Shen, Fei Jiang (2023). Robust Recommendation Via Social Network Enhanced Matrix Completion, Statistica Sinica, 33(2), 609-631.
  • Xin Chen, Dan Yang, Yan Xu, Yin Xia, Dong Wang, H. Shen (2023). Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data, Journal of Econometrics, 232(2), 544-564.
  • Seonjoo Lee, H. Shen, Young Truong (2021). Sampling Properties of Color Independent Component Analysis, Journal of Multivariate Analysis, 181, 104692.
  • Yi He, Yanxi Hou, Liang Peng, H. Shen (2020). Inference for Conditional Value-at-Risk of a Predictive Regression, Annals of Statistics, 48, 3442-3464.
  • Han Ye, Lawrence D. Brown, H. Shen (2020). Hazard Rate Estimation for Call Center Customer Patience Time, IISE Transactions, 52, 890-903.
  • Fei Jiang, Qing Cheng, Guosheng Yin, H. Shen (2020). Functional Censored Quantile Regression, Journal of the American Statistical Association, 115, 931-944.
  • J. Cai, A. Mandelbaum, C. H. Nagaraja, H. Shen, L. Z. Zhao (2019). Statistical Theory Powering Data Science, Statistical Science, 34, 669-691.
  • Han Ye*, James Luedtke, H. Shen (2019). Call Center Arrivals: When to Jointly Forecast Multiple Streams?, Production and Operations Management, 28, 27-42.
  • Zheng, Wang, Wang, Li, Wang, Zhao, H. Shen, Wang, Zuo, Pan, Wang, Shi, Ju, Liu, Dong, Wang, Sui, Xue, Pan, Niu, Luo, Wang, Feng, Wang (2019). The Efficacy and Safety of Nimodipine in Acute Ischemic Stroke Patients with Mild Cognitive Impairment: A Double-blind, Randomized, Placebo-controlled Trial, Science Bulletin, 64, 101-107.
  • Gen Li*, J. Z. Huang, H. Shen (2018). To Wait or Not to Wait: Two-Way Functional Hazards Model for Understanding Waiting in Call Centers, Journal of the American Statistical Association, 113, 1503-1514.
  • Wang, Li, Zhao, Wang, Wang, Wang, Liang, Liu, Wang, Li, H. Shen, Bettger, Pan, Jiang, Yang, Zhang, Fonarow, Peterson, Schwamm, Xian, Wang (2018). Effect of a Multifaceted Quality Improvement Intervention on Hospital Personnel Adherence to Performance Measures in Patients With Acute Ischemic Stroke in China: A Randomized Clinical Trial, The Journal of the American Medical Association, 320, 245-254.
  • Jiang, Jiang, Zhi, Dong, Li, Ma, Wang, Dong, H. Shen, Wang (2017). Artificial Intelligence in Healthcare: Past, Present, and Future, Stroke and Vascular Neurology, 1-14.
    (Won the Most Influential Publication Award from the China Stroke Association.)
  • Z. Li, C. Wang, X. Zhao, L. Liu, C. Wang, H. Li, H. Shen, …, Yongjun Wang (2016). Substantial Progress Yet Significant Opportunity for Improvement in Stroke Care in China, Stroke, 47, 2843-2849.
  • Rouba Ibrahim, Pierre L’Ecuyer, H. Shen, Mamadou Thiongane (2016). Inter-Dependent, Heterogeneous, and Time-Varying Service-Time Distributions in Call Centers, European Journal of Operational Research, 250, 480-492.
  • Dan Shen*, H. Shen, J. S. Marron (2016). A General Framework for Consistency of Principal Component Analysis, Journal of Machine Learning Research, 17, 1-34.
  • Noah Gans, H. Shen, Yong-Pin Zhou, Nikolay Korolev, Alan McCord, Herbert Ristock  (2015). Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling, Manufacturing & Service Operations Management, 17, 571-588.
  • Yilong Wang, Zixiao Li, Ying Xian, Xingquan Zhao, Hao Li, H. Shen, …, Yongjun Wang (2015). Rationale and Design of a Cluster-Randomized Multifaceted Intervention Trial to Improve Stroke Care Quality in China: The GOLDEN BRIDGE-AIS, American Heart Journal, 169, 767-774.
  • Ruijun Ji, David Wang, H. Shen, Yuesong Pan, Gaifen Liu, Penglian Wang, Yilong Wang, Hao Li, Yongjun Wang (2013). Interrelationship Among Common Medical Complications After Acute Stroke: Pneumonia Plays an Important Role, Stroke, 44, 3436-3444.
  • Ruijun Ji, H. Shen, Yuesong Pan, Penglian Wang, Gaifen Liu, Yilong Wang, Hao Li, Yongjun Wang (2013). A Novel Risk Score to Predict Pneumonia after Acute Ischemic Stroke, Stroke, 44, 1303-1309.
  • Lingsong Zhang, H. Shen, Jianhua Z. Huang (2013). Robust Regularized Singular Value Decomposition with Application to Mortality Data, The Annals of Applied Statistics, 7, 1540-1561.
  • Spencer Hays, H. Shen, Jianhua Z. Huang (2012). Functional Dynamic Factor Models with Application to Yield Curve Forecasting, The Annals of Applied Statistics, 6, 870-894.
  • Seonjoo Lee, H. Shen, Young Truong, Michelle Lewis, Xuemei Huang (2011). Independent Component Analysis Involving Auto-correlated Sources with an Application to Functional Magnetic Resonance Imaging, Journal of the American Statistical Association, 106, 1009-1024.
  • Noah Gans, Nan Liu, Avishai Mandelbaum, H. Shen, Han Ye (2010). Service Times in Call Centers: Agent Heterogeneity and Learning with Some Operational Consequences, A Festschrift for Lawrence D. Brown, IMS Collections, 6, 99-123.
  • Jianhua Z. Huang, H. Shen, Andreas Buja (2009). The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions, Journal of the American Statistical Association, 104, 1609-1620.
  • H. Shen, Jianhua Z. Huang (2008). Interday Forecasting and Intraday Updating of Call Center Arrivals, Manufacturing & Service Operations Management, 10, 391-410.
  • H. Shen, Jianhua Z. Huang (2008). Forecasting Time Series of Inhomogeneous Poisson Processes with Application to Call Center Workforce Management, The Annals of Applied Statistics, 2, 601-623.
  • Lawrence D. Brown, Noah Gans, Avishai Mandelbaum, Anat Sakov, H. Shen, Sergey Zeltyn, Linda H. Zhao (2005). Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective, Journal of the American Statistical Association, 100, 36-50.
Awards and Honours
  • Faculty Special Contribution Teaching Award, Faculty of Business and Economics (now HKU Business School), 2018.
  • The Most Influential Publication Award, China Stroke Association, 2018.
  • Best Advisor of the Year Award, Academy of Asian Business, 2018.
  • Fellow, American Statistical Association, 2015.
  • Elected Member, International Statistical Institute, 2015.
  • Cluster Chair for Big Data Analytics, INFORMS International, 2015.
  • Program Chair-Elect, 2014, Section on Statistics in Imaging, American Statistical Association, 2013.
  • Issue Feature Article, Journal of Computational and Graphical Statistics, 2014.
  • Awarded University Affairs Committee Grant from The Xerox Foundation, 2012.
  • Most Cited Article of Journal of Multivariate Analysis since 2008, 2012.
  • Randy Sitter Paper of 2010 in Technometrics, 2010.
  • Awarded Challenge Grant from National Institute on Drug Abuse, 2009.
  • UNC-CH R. J. Reynolds Fund Award for Junior Faculty Development, 2008.
  • J. Parker Bursk Memorial Prize for the best PhD student in the Department of Statistics, University of Pennsylvania, 2002.
Service to the University/Community
  • Associate Editor, Management Science, Stochastic Modeling and Simulation, 2014 – present
  • Associate Editor, Journal of the American Statistical Association, 2014 – present
  • Associate Editor, Technometrics, 2013 – present
  • Associate Editor, The Annals of Applied Statistics, 2011 – present
  • Associate Editor, Management Science, Special Issue on Business Analytics, 2012
Recent Publications
Testing and Support Recovery of Correlation Structures for Matrix-valued Observations With an Application to Stock Market Data

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

Hazard Rate Estimation for Call Center Customer Patience Time

Estimating the hazard function of customer patience time has become a necessary component of effective operational planning such as workforce staffing and scheduling in call centers. When customers get served, their patience times are right-censored. In addition, the exact event times in call centers are sometimes unobserved and naturally binned into time intervals, due to the design of data collection systems. We develop a TunT (Transform-unTransform) estimator that turns the difficult problem of nonparametric hazard function estimation into a regression problem on binned and right-censored data. Our approach starts with binning event times and transforming event count data with a mean-matching transformation, which enables a simpler characterization of the heteroscedastic variance function. A nonparametric regression technique is then applied to the transformed data. Finally, the estimated regression function is back-transformed to yield an estimator for the original hazard function. The proposed estimation procedure is illustrated using call center data to reveal interesting customer patience behavior, and health insurance plan trial data to compare the effect between treatment and control groups. The numerical study shows that our approach yields more accurate estimates and better staffing decisions than existing methods.

HKU Business School at the heart of medical revolution

Innovation in healthcare is forever changing how we see and experience the medical industry. The environment is offering HKU’s Faculty of Business and Economics (the Faculty) a unique opportunity to be at the forefront of utilising rich data, creating better health outcomes for everyone.

Big data is rewriting the medical future of millions of people

Patients in China suffering from acute ischemic stroke, when arteries leading to the brain are blocked, have traditionally not experienced excellent clinical outcomes. Battling this disease has been a long-term battle for physicians working in the country’s overcrowded under-resourced public hospitals. Professor Haipeng Shen, Patrick S C Poon Professor in Analytics and Innovation at HKU Business School, has been working to change this situation by collaborating with top physicians and embracing the power of big data.

Functional Censored Quantile Regression

We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.


Professor Haipeng SHEN, Patrick S C Poon Professor in Analytics and Innovation, was interviewed by four media outlets on his recent research in data-driven decision making in healthcare management.