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Dr. Xiaoming Li, Systems Engineering, Best Researcher Award

Doctorate at Concordia University, Canada

Summary:

Dr. Xiaoming Li is an accomplished researcher and educator with extensive experience in artificial intelligence, machine learning, and optimization models for shared mobility and smart logistics. With a Ph.D. in Information and Systems Engineering from Concordia University, his work focuses on integrating data-driven approaches to optimize urban transportation systems and enhance sustainability. He has a strong background in software engineering and has contributed significantly to both academic research and industry projects. Dr. Li has received several awards, including the IEEE Outstanding Leadership Award and the Mitacs Accelerate Program Award. As a dedicated educator, he has taught and mentored students across various institutions, shaping the next generation of computer scientists.

Professional Profile:

👩‍🎓Education:

Ph.D. in Information and Systems Engineering

  • Concordia University, MontrĂ©al, QC, Canada
  • Cumulative GPA: 4.0 / 4.3

M.Sc. in Computer Software and Theory

  • Northeastern University, China
  • Cumulative GPA: top 16.7%

B.Sc. in Computer Science and Technology

  • Shenyang Aerospace University, China

Professional Experience:

Dr. Xiaoming Li currently serves as a Research Associate at Concordia University, Montréal, where he supervises interdisciplinary research in artificial intelligence and operations research for shared mobility-on-demand applications. His role involves designing deep learning models for time-series demand forecasts, developing data-driven stochastic optimization models for renewable energy mobility management, and creating an integrated optimization framework for sustainable crowd-shipping services. Previously, Dr. Li held a Research Assistant position at Concordia, working on data analysis and machine learning model development for various projects, including forecasting commodity rates and predicting hotel booking cancellations.

Dr. Li’s industrial experience includes internships at Ericsson’s Global Artificial Intelligence Accelerator, where he developed an energy-saving framework for 5G base stations, and Medialpha, where he optimized nurse routing and medical resource allocation using meta-heuristic algorithms. Additionally, he has substantial experience as a Software Engineer at Shenyang Aerospace University, leading projects and managing teams.

In academia, Dr. Li has served as an Adjunct Faculty at Vanier College, teaching database theory and application development, and as a Full-Time Lecturer at Shenyang Aerospace University, instructing on a variety of computer science courses and supervising numerous student projects.

Research Interest:

  • Machine Learning
  • Operations Research
  • Data Science
  • Data-Driven Optimization
  • Agent-Based Simulation Modeling
  • Shared Mobility
  • Smart Logistics
  • Sustainable Intelligent Transportation Systems

Publications Top Noted: 

GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems

  • N. Lin, Y. Fan, L. Zhao, X. Li, M. Guizani
  • IEEE Transactions on Mobile Computing, 14, 2022.

BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems

  • J. Gao, X. Li, C. Wang, X. Huang
  • IEEE Transactions on Intelligent Transportation Systems, 23(8), 11666-11676, 2021.

Driver Guidance and Rebalancing in Ride-Hailing Systems Through Mixture Density Networks and Stochastic Programming

  • X. Li, J. Gao, C. Wang, X. Huang, Y. Nie
  • 2021 IEEE International Smart Cities Conference (ISC2), 1-7, 2021.

Learning-Based Open Driver Guidance and Rebalancing for Reducing Riders’ Wait Time in Ride-Hailing Platforms

  • J. Gao, X. Li, C. Wang, X. Huang
  • 2020 IEEE International Smart Cities Conference (ISC2), 1-7, 2020.

Ride-Sharing Matching Under Travel Time Uncertainty Through Data-Driven Robust Optimization

  • X. Li, J. Gao, C. Wang, X. Huang, Y. Nie
  • IEEE Access, 10, 116931-116941, 2022.
Xiaoming Li | Systems Engineering | Best Researcher Award

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