People

Dr. Wang received his PhD in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology (HKUST). He earned his bachelor's and master's degrees from Peking University. His research interests include deep learning, natural language processing, and their applications in business analytics, operations management, and other areas. Prior to joining EDUHK, he worked as an Associate Professor at the Hang Seng University of Hong Kong and a Research Assistant Professor at HKUST. His Erdős number is 3.

• Methodological:
  • o Deep learning
  • o Natural language processing
  • o Statistical modeling
  • o Lab / field experiments
  • o Operations research
• Substantive:
  • o Business intelligence
  • o Education analytics
  • o Healthcare informatics
  • o User experience and decision making

• Huang, J.*, Y. Wang (corresponding author), S. C. H. Ng, and F. Tsung. 2024. Overcoming the Semantic Gap in the Customer-to-Manufacturer (C2M) Platform: A Soft Prompts-Based Approach with Pretrained Language Models, International Journal of Production Economics, accepted.
• Tang, X.*, Y. Tian, C.-H. Wu, Y. Wang, Z.-B. Mian. 2024. A New Class of Zero-truncated Counting Models and Its Application, Communications in Statistics – Simulation and Computation, accepted.
• Huang, S.*, Y. Wang (corresponding author), E. Y. C. Wong, and L. Yu. 2024. Ensemble Learning with Soft-Prompted Large Language Model for Fact Checking, Natural Language Processing Journal, accepted.
• Peng, Z.*, M. Li*, Y. Wang (corresponding author), and G. Ho. 2023. Combating the COVID-19 Infodemic Using Prompt-Based Curriculum Learning, Expert Systems with Applications, accepted.
• Wang, Y., D. Mo and H. Ma. 2023. Perception of Time in the Online Product Customization Process. Industrial Management & Data Systems, 123(2), 369-385.
• Wu, C.H., Y. Wang (corresponding author), and J. Ma. 2023. Maximal Marginal Relevance-Based Recommendation for Product Customization. Enterprise Information Systems, 17(5), 1992018.
• Mo, D., Y. Tseng, Y. Wang and W. Xu. 2023. Online Reinforcement Learning-Based Inventory Control for  Intelligent E-Fulfilment Dealing with Nonstationary Demand, Enterprise Information Systems, accepted.
• Luo, L.*,Y. Wang (corresponding author), and D. Mo. 2023. Identifying Heart Disease Risk Factors from Electronic Health Records Using an Ensemble of Deep Learning Method, IISE Transactions on Healthcare Systems Engineering, accepted.
• Ma, H., Y. Sun, D. Mo and Y. Wang. 2023. Impact of Passenger Unused Baggage Capacity on Air Cargo Delivery. Annals of Operations Research, accepted.
• Wang, Y. (corresponding author), X. Li, L. Zhang and D. Mo. 2022. Configuring Products With Natural Language: A Simple Yet Effective Approach Based on Text Embeddings and Multilayer Perceptron. International Journal of Production Research, 60(17): 5394 - 5406.
• Wang, Y. (corresponding author), L. Luo* and H. Liu. 2022. Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-generated Content. IEEE Transactions on Engineering Management, 69(4): 1622 - 1634.
• Luo, L.*, Y. Wang (corresponding author), and H. Liu. 2022. COVID-19 Personal Health Mention Detection from Tweets Using Dual Convolutional Neural Network, Expert Systems with Applications, 200: 117139.
• Mo. D. Y., Y. Wang, D. Ho and K. H. Leung. 2022. Redeploying Excess Inventories with Lateral and Reverse Transshipments. International Journal of Production Research, 60(10): 3031-3046.
• Wong, W. K., S. Ye, H. Liu, Y. Wang. 2022. Effective Mobile Target Searching Using Robots. Mobile Networks and Applications, 27: 249-265.
• Wang, Y. (corresponding author), X. Li and D. Mo. 2021. Knowledge-Empowered Multi-Task Learning to Address the Semantic Gap Between Customer Needs and Design Specifications. IEEE Transactions on Industrial Informatics, 17(12): 8397 - 8405.
• Wang, Y. (corresponding author), W. Zhao and X. Wan. 2021. Needs-based Product Configurator Design for Mass Customization Using Hierarchical Attention Network. IEEE Transactions on Automation Science and Engineering, 18(1): 195-204.
• Wang, Y. (corresponding author), C. Wong, T. Cheung and E. Wu. 2021. How Influential Factors Affect Aviation Networks: A Bayesian Network Analysis. Journal of Air Transport Management, 91: 101995.
• Wang, Y. (corresponding author), and X. Li. 2021. Mining Product Reviews for Needs-Based Product Configurator Design: A Transfer Learning-Based Approach. IEEE Transactions on Industrial Informatics, 17(9): 6192 - 6199.
• Wang, Y. (corresponding author), X. Li and F. Tsung. 2020. Configuration-based Smart Customization Service: A Multitask Learning Approach. IEEE Transactions on Automation Science and Engineering, 17(4): 2038-2047.
• Mo, D., Y. Wang, L. Leung and M. Tseng. 2020. Optimal Service Parts Contract with Multiple Response Times and On-site Spare Parts. International Journal of Production Research, 58(10): 3049-3065.
• Mo, D., Y. Wang, and M. Tseng. 2020. Mass Customizing Paratransit Services with a Ridesharing Option. IEEE Transactions on Engineering Management, 67(1): 234-245.

• Mo, D., D. Ho, E. Wong and Y. Wang. 2022. Adaptive Intelligent Redeployment Strategy for Service Parts Inventory Management. Intelligent Engineering and Management for Industry 4.0, Springer, Cham, pp 107–115
• Tseng, M. M., Y. Wang and R. J. Jiao. 2018. Mass Customization. CIRP Encyclopedia of Production Engineering, Springer Publications.
• Tseng, M. M., Y. Wang and R. J. Jiao. 2017. Modular Design. CIRP Encyclopedia of Production Engineering, Springer Publications.
• Tseng, M. M., Y. Wang and C. Wang 2015. Seasons of Mass Customization, Winter or Spring? KDI/EWC series on Economic Policy, Edward Elgar Publications.

• PI: “Generating Novel Customer Needs for New Product Development,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E08/23, January 2024 – June 2026.
• PI: “MetaConfigurator: A Resource-Effective Method to Develop Needs-Based Configurators for Product Customisation,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E05/22, January 2023 – December 2025.
• PI: “AutoQFD: A Smart Quality Function Deployment Method for Product Development,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E08/21, January 2022 – December 2024.
• PI: “Generalised Needs-based Product Configurator Design,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E06/18, January 2019 – June 2021.
• PI: “Configuration-based Recommendation for Online Product Customization in E-commerce,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E07/17, January 2018 – June 2020.
• PI: “Relative Attribute Based Configurator Design for Mass Customization,” Hong Kong Research Grant Council, Faculty Development Scheme, Project No. UGC/FDS14/E02/15, January 2016 – June 2018.