Dr. Xiaoming Li

Dr. Xiaoming Li obtained his Ph.D. from Concordia Institute for Information Engineering (CIISE) in Montreal. He also holds master and bachelor degrees in Computer Science and Technology. Dr. Li has over 20 years college-level teaching experience with over 20 courses in Computer Science, Data Science, and Applied Math fields.  He also has over 10 years industrial experience as a software engineer, data scientist, and research scientist. Prior to joining UCW, Dr. Li was a postdoctoral researcher at Purdue University, conducting research on national digital twins for logistics and supply chain systems. He has published over 20 conference and journal papers as well as one patent from Ericsson Inc.

Expertise and Experience

Dr. Li’s research focuses on operations research, data science, machine learning, and large-scale data-driven optimization and simulation, with practical applications in urban mobility, logistics, and supply chain systems. In addition to his academic career, Dr. Li has served as an applied scientist and industry consultant, bridging theoretical advances with real-world challenges and delivering innovative, data-driven solutions for complex systems.

Selected Publications and Scholarly Activity

Conference Proceedings

• X. Li, C. Wang, and X. Huang. “A Short-Term Charging Waiting Time Estimation Approach using Recurrent Mixture Density Networks and Queuing Model,” 2024 Annual Modeling and Simulation Conference (ANNSIM-2024).
• X. Li, H. Taillon, C. Wang, and X. Huang. “Demand Density Forecasting in Mobility-on-Demand through Recurrent Mixture Density Networks,” 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC-2023). IEEE, 2023, pp.250–255.
• X. Li, J. Gao, C. Wang, X. Huang, and Y. Nie, “Order Dispatching in Ride-Sharing Platform under Travel Time Uncertainty: A Data-Driven Robust Optimization Approach,” in 2021 IEEE International Conference on Autonomous Systems (ICAS-2021). IEEE, 2021, pp.1–7.
• X. Li, J. Gao, C. Wang, X. Huang, and Y. Nie, “Ride-Sharing Matching under Travel Time Uncertainty through A Data-Driven Robust Optimization Approach,” in 2021 IEEE 24th International Conference on Intelligent Transportation Systems (ITSC-2021). IEEE, 2021, pp. 3420–3425.
• X. Li, J. Gao, C. Wang, X. Huang, and Y. Nie, “Driver Guidance and Rebalancing in Ride-Hailing Systems through Mixture Density Networks and Stochastic Programming,” in 2021 IEEE 7th International Smart Cities Conference (ISC2-2021). IEEE, 2021, pp. 1–7.
• X. Li, C. Wang, and X. Huang, “Reducing Car-Sharing Relocation Cost through Non-Parametric Density Estimation and Stochastic Programming,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC-2020). IEEE, 2020, pp. 1–6.
• X. Li, C. Wang, X. Huang, and Y. Nie, “A Data-Driven Dynamic Stochastic Programming Framework for Ride-Sharing Rebalancing Problem under Demand Uncertainty,” in 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications (ISPA-2020). IEEE, 2020, pp. 1120–1125.
• J. Gao, X. Li, C. Wang, and X. Huang, “Learning-Based Open Driver Guidance and Rebalancing for Reducing Riders’ Wait Time in Ride-Hailing Platforms,” in 2020 IEEE 6th International Smart Cities Conference (ISC2-2020). IEEE, pp. 1–7.

Journal Articles

• X. Li, H. Taillon, C. Wang, and X. Huang. “XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting Considering High Volatility,” IEEE Transactions on Intelligent Vehicles, vol. 10, pp. 1855-1866, 2025.
• X. Li, H. Taillon, C. Wang, and X. Huang. “BM-RCWTSG: An Integrated Matching Framework for Electric Vehicle Ride-Hailing Services under Stochastic Guidance,” Sustainable Cities and Society (2024) 105485.
• X. Li, J. Gao, C. Wang, X. Huang, Y. Nie, “MDN-Enabled SO for Vehicle Proactive Guidance in Ride-Hailing Systems: Minimizing Travel Distance and Wait Time,” IEEE Systems, Man, and Cybernetics Magazine 9 (3) (2023) 28–36.
• X. Li, J. Gao, C. Wang, X. Huang, and Y. Nie, “Ride-Sharing Matching under Travel Time Uncertainty through Data-Driven Robust Optimization,” IEEE Access, vol. 10, pp. 116 931–116 941, 2022.
• X. Li, J. Gao, C. Wang, X. Huang, “A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios,” International Journal of Intelligent Transportation Systems Research. pp.1–15, 2022.
• N. Lin, Y. Fan, L. Zhao, X. Li, “A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems,” IEEE Transactions on Mobile Computing, 2022.
• J. Gao, X. Li, C. Wang, X. Huang, “A Pricing Mechanism for Ride-Hailing Systems in The Presence of Driver Acceptance Uncertainty,” IEEE Access, vol. 10, pp. 83017–83028, 2022.
• X. Li and L. Zhao, “Towards Smart Transportation: A Learning-Based Data-Driven Optimization Approach for Electric Taxi Dispatch Problem,” Internet Technology Letters, vol. 4, no. 1, p. e164, 2021.
• J. Gao, X. Li, C. Wang, X. Huang, “BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11 666–11 676, 2021.
• X. Liu, Y. Li, X. Li, C. Tian, and Y. Yang, “Multi-Feature Consultation Model for Human Action Recognition in Depth Video Sequence,” Journal of Engineering, vol. 2018, no. 16, pp. 1498–1502, 2018.

Awards

• IEEE Outstanding Leadership Award from the 9th IEEE International Conference on Smart City and Informatization (2021)
• Mitacs Accelerate Program Award (2018, 2019)
• Concordia Institute for Information Systems Engineering Graduate Studies Award (2017)
• Concordia International Tuition Award of Excellence (2017)