I am Qi Luo, an Assistant Professor at Clemson University.
E-mail: qluo2 (at) clemson.edu Office: 277B Freeman
He was a Postdoc at Cornell University and a researcher in Argonne National Laboratory – University of Chicago. He received his Ph.D. in Operations Research and Scientific Computing from the University of Michigan, and B.S. from Xi’an Jiaotong University.
His primary research interest is in the areas of data-driven decision-making in operations management and emerging mobility systems. His research has been recognized by prizes such as the Kikuchi-Karlaftis Best Paper Award, the INFORMS APS Best Student Paper (finalist) and I-Sim Best Student Paper.
Dr. Luo is a faculty member affiliated with Clemson Operations Research Institute and the Artificial Intelligence Research Institute for Science and Engineering (AIRISE).
- DIALab in Industrial Engineering at Clemson University is actively looking for Ph.D./M.S. students with sound backgrounds in mathematical modeling and data analytics (Enroll in 2023).
- Please directly apply to IE graduate program.
- The most important quality is self-motivation — I appreciate a solid research plan in your email with an attached CV.
- rl-transport.org I created two functional tools for disseminating the recent development of data-driven and reinforcement learning methods in transportation research.
- FY 2022 – 2023:
- Welcome Yan Wu to join our lab! She is co-advised by Dr. Yuyuan (Lance) Ouyang from SMSS and she obtained a M.S. degree from OR in SMSS at Clemson University in 2022. She will work on reinforcement learning and inverse optimization with applications in transportation and healthcare.
- Dr. Luo (co-PI) was awarded a NSF grant ($150,000) titled “Adapting to the Future of Robotic Surgery: Understanding Training and Design Environments for Human-Robot Teams“.
- Dr. Luo (co-PI) was awarded a SAGES Robotics grant ($50,000) titled “Enter Personalized Robotic Surgery Training to Optimize Learning Outcomes“.
- FY 2021 – 2022:
- Welcome Hanwen Liu to join our lab! He graduated with a M.S. from the University of Florida and will work on data-driven decision-making in human-AI interaction, resilience, and transportation systems.
- Dr. Luo and his collaborators won the 2021 Kikuchi-Karlaftis Best Paper Award from the TRB Artificial Intelligence and Advanced Computing Applications (AIACA) committee for their work ‘Optimizing matching time intervals for ride-hailing services using reinforcement learning.‘
- FY 2020-2021:
- Dr. Luo is in the organizing committee for the 2021 International Symposium on Transportation Data and Modeling (ISTDM) on June 21–23, 2021 in Ann Arbor, Michigan.
- Dr. Luo is in an organizing committee for Emerging Mobility Systems and Services Seminar Series, a prestigious research seminar for advanced transportation studies. This weekly seminar is free to register. (archived link)
- How to mitigate the risk of taking public transit and distribute vaccines during the outbreak of pandemic? We are working with researchers from Weill Cornell Medicine, Vanderbilt University, and Rutgers University on modeling the best practice in addressing this timely issue.
- FY 2019-2020:
- Joint work with Prof. Zhibin Chen (NYU Shanghai), Prof. Yafeng Yin (CEE U-M), and Prof. Romesh Saigal is reported on U-M Engineering Research News in 2020.
- Joint work with Zhiyuan Huang and Prof. Henry Lam titled “Dynamic Congestion Pricing for Ridesourcing Traffic: A Simulation-Based Approach.” won the Winter Simulation Conference I-Sim Ph.D. Colloquium Best Student Paper Award in 2019.
- Hao Yuan’s work co-authored with Qi Luo and Prof. Cong Shi titled “Marrying Stochastic Gradient Descent with Bandits: Learning Algorithms for Inventory Systems with Fixed Costs.” won the 2019 Applied Probability Society Best Student Paper competition, finalist.
- Joint work with Xuechun Dou, Prof. Sharon Di (Columbia University), and Prof. Robert C. Hampshire on connecting bikesharing and ride-hailing was reported in Ford School’s News.
DIALab (Decision Intelligence & Analytics Lab) at Clemson University focuses on exploring the mathematical foundations that comprise prescriptive analytics and learning algorithms.
Our ongoing projects include building emerging mobility ecosystems, managing personalized healthcare services, and improving digital supply chains.
Research & Publication
Emerging Mobility Market
Reinvent passenger and goods transportation in the digital age
Refereed Articles Published
- Dong, Tingting, Zhengtian Xu, Qi Luo, Yafeng Yin, Jian Wang, and Jieping Ye. “Optimal contract design for ride-sourcing services under dual sourcing.” Transportation Research Part B: Methodological 146 (2021): 289-313.
- Luo, Qi, Romesh Saigal, Zhibin Chen, and Yafeng Yin, “Accelerating the Adoption of Automated Vehicles by Subsidies: A Dynamic Games Approach. ” Transportation Research Part B: Methodological, 129:226-243, 2019.
- Li, Shukai, Qi Luo, and Robert Hampshire. “Optimizing Large On-demand Transportation Systems through Stochastic Conic Programming.” European Journal of Operational Research (2021) 295.2 (2021): 427-442.
- Luo, Qi, Xuechun Dou, Xuan Di, and Robert Hampshire. “Multimodal Connections between Micro-Mobility and Micro-transit: Conceptual Foundations and Empirical Evidence.” IEEE Intelligent Transportation Systems Magazine, forthcoming.
- Luo, Qi, Shukai Li, and Robert Hampshire. “Optimal Design of Intermodal Mobility Networks under Uncertainty: Connecting Micromobility with Mobility-on-Demand Transit.” EURO Journal on Transportation and Logistics (2021): 100045.
Refereed Conference Proceedings
- Luo, Qi, Samitha Samaranayake, and Siddhartha Banerjee. “Multimodal Mobility Systems: Joint Optimization of Transit Network Design and Pricing.” Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS). 2021.
- Luo, Qi, Xuechun Dou, Xuan Di, and Robert Cornelius Hampshire. ”Multimodal Connections between Dockless Bike-sharing and Ride-Hailing: An Empirical Study in New York City.” In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2256-2261. IEEE, 2018.
- Luo, Qi, Romesh Saigal, Robert Hampshire, and Xinyi Wu. ”A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars.” In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pp. 1-5. IEEE, 2017.
- Wu, Xinyi, Kartik Balkumar, Qi Luo, Robert Hampshire, and Romesh Saigal. “An Evaluation of Information Sharing Parking Guidance Policies Using a Bayesian Approach.” TransportationResearch Board Annual Meeting, Washington DC 2016.
- Liu, Yang, Qi Luo, Raga Gopalakrishnan, and Samitha Samaranayake. “A Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides.” arXiv preprint arXiv:2112.14297 (2021).
- Banerjee, Siddhartha, Chamsi Hssaine, Qi Luo, and Samitha Samaranayake. “Plan Your System and Price for Free: Fast Algorithms for Multimodal Transit Operations.” Available at SSRN (2021).
- Luo, Qi, Viswanath Nagarajan, Alexander Sundt, Yafeng Yin, John Vincent, and Mehrdad Shahabi. “Efficient Algorithms for Stochastic Ridepooling Assignment with Mixed Fleets.” arXiv preprint arXiv:2108.08651 (2021).
- Dong, Tingting, Xiaotong Sun, Qi Luo, Jian Wang, and Yafeng Yin. “Modeling team competition on on-demand service platforms.” Available at SSRN (2021).
- Sundt, Alexander, Qi Luo, John Vincent, Mehrdad Shahabi, and Yafeng Yin. “Heuristics for Customer-focused Ride-pooling Assignment.” Submitted.
Explore and exploit when interacting with the environment
Refereed Articles Published
- Yuan, Hao, Qi Luo, and Cong Shi. “Marrying Stochastic Gradient Descent with Bandits: Learning Algorithms for Inventory Systems with Fixed Costs.” Management Science (2021) doi.org/10.1287/mnsc.2020.3799.
- INFORMS APS 2019 Student paper competition, finalist.
- IOE at U-Mich Murty Prize 2019 for best paper on optimization.
- Qin, Guoyang, Qi Luo, Yafeng Yin, Jian Sun, and Jieping Ye. “Optimizing matching time intervals for ride-hailing services using reinforcement learning.” Transportation Research Part C: Emerging Technologies 129 (2021): 103239.
- TRB 2021 Kikuchi-Karlaftis Best Paper Award.
- Li, Shukai, Qi Luo, Zhiyuan Huang, and Cong Shi. “Online Learning for Constrained Assortment Optimization under Markov Chain Choice Model”.
Workshops and Invited Presentations:
- Qin, Guoyang, Qi Luo, Yafeng Yin, Jian Sun, and Jieping Ye. “Optimal Matching Time Interval Policy for Ride-hailing Services using Reinforcement Learning.” 100th Annual Meeting Transportation Research Board, Washington 2021.
- Yuan, Hao, Qi Luo, and Robert Hampshire. “Data-Driven Modeling of Ride-Hailing Trajectories.” Workshop in ACM SIGIR conference, Ann Arbor 2018.
Human-Engaged Models for Healthcare and Public Policy
Human-centric models can make a difference in social good
Referred Article Published
- Luo, Qi, Yunlei Yin, Pengyu Chen, Zhenfei Zhan, and Romesh Saigal. “Dynamic subsidies for synergistic development of charging infrastructure and electric vehicle adoption.” Transport Policy (2022).
- Luo, Qi, Marissa Gee, Daniel Work, Benedetto Piccoli, and Samitha Samaranayakee. “Managing public transit during a pandemic: the trade-off between safety and mobility” Transportation Research Part C: Emerging Technologies (2022).
- Luo, Qi, Ryan Weightman, Sean T. McQuade, Mateo Díaz, Emmanuel Trélat, William Barbour, Dan Work, Samitha Samaranayake, and Benedetto Piccoli. “Optimization of vaccination for COVID-19 in the midst of a pandemic.” Networks and Heterogeneous Media 17, no. 3 (2022): 443.
- Luo, Qi, and Romesh Saigal. “Multiagent Incentive Contracts: Existence, Uniqueness and Implementation.” Mathematics 2021, 9, 19.
Refereed Conference Proceedings
- Luo, Qi, Zhiyuan Huang, and Henry Lam. “Dynamic Congestion Pricing for Ridesourcing Traffic: A Simulation-Based Approach.” Winter Simulation Conference 2019 Proceedings.
- Winter Simulation Conference 2019 I-Sim Ph.D. Colloquium Best Student Paper Award, winner
- Luo, Qi, Marissa Gee, Daniel Work, Benedetto Piccoli, and Samitha Samaranayakee. “Managing Public Transit in the Prevalence of Pandemic and Reopening the Economy.” 100th Annual Meeting Transportation Research Board, Washington 2021.
- Yu, Fangzhou, Qi Luo, Tayo Fabusuyi, and Robert Hampshire. “A Heuristic for Learn-and-Optimize New Mobility Services with Equity and Efficiency Metrics.” 99th Annual Meeting Transportation Research Board, Washington 2020.
- Hampshire, Robert C., Carol Flannagan, H.V. Jagadish, Tayo Fabusuyi, Andong Chen, Eric Hanss, Oliver He, Kathleen D. Klinich, Qi Luo, Aditi Misra, and Matthew P. Reed. “A Public Health Informed Approach to Transportation Equity.” Submitted.
- Yunlei Yin, Qi Luo, Zhenfei Zhan, and Romesh Saigal, “Dynamic Subsidies for a Synergy between Charging Infrastructure Development and Electric Vehicle Adoption.” Submitted.
Reinforcement Learning in Transportation Research
Here are useful references for researchers who are interested in using RL for transportation research (some are recommended by my students).
My graduate course IE 8490: Markov Decision Processes and Reinforcement Learning:
- Syllabus (2022, 2023)
- Problem formulation (stochastic shortest path, inventory control)
- Finite-horizon MDP
- Infinite-horizon MDP
- Temporal difference learning and Q-learning
- Stochastic gradient descent
- Multi-armed bandit
- Target audience: graduate students in IE, EE, CEE, CS, and applied mathematics.
- Lecture notes
I am not the author of the following sites but disseminate to anyone interested in this field:
- Optimization (especially continuous and nonlinear optimization) is the foundation of RL:
- Wiki for Optimization: Cornell University Computational Optimization Open TextbookDynamic Programming and Optimization in Game Theory
- Optimization for Machine Learning and Data Analytics
- RL is a burgeoning field:
- ML/RL Notes and Database: Super Machine Learning Revision Notes
- Data plays a significant role in RL-powered algorithms
- Workshop and tutorials:
- Research communities and professional organizations:
- Markov Decision Processes and Reinforcement Learning (IE 8490, Clemson University), Spring, 2022.
- Deterministic Operations Research (IE 3800, Clemson University), Spring/Fall, 2021.
- Introduction to Optimization Methods (IOE 310, University of Michigan), Instructor of Record
- Industrial and Operations Engineering, University of Michigan. Winter, 2018.
- Transportation Network Modeling (CEE 559, University of Michigan), Guest Lecturer
- Civil and Environmental Engineering, University of Michigan. Winter, 2019.
Journal Editorial Services (reviewer)
|Transportation Science||2022 – current|
|Management Science||2021 – current|
|IISE Transactions||2022 – current|
|Transportation Research Part B: Methodological||2021 – current|
|Transportation Research Part C: Emerging Technologies||2017 – current|
|Transportation Research Part E: Logistics and Transportation Review||2018 – current|
|Networks and Spatial Economics||2022 – current|
|IEEE Transactions on Intelligent Transportation Systems||2018 – current|
|Service Science||2020 – current|
|European Journal of Operational Research||2020 – current|
|Production and Operations Management||2020 – current|
|ASME Journal of Manufacturing Science and Engineering||2022 – current|
|Omega||2021 – current|
|EURO Journal on Transportation and Logistics||2020 – current|
|Networks||2021 – current|
|Transportation Research Record||2020 – current|
|Journal of Systems Science and Complexity||2017|
Conference Session Chair and Editorial Services
|ISTDM 2021||Organizing committee member, International Symposium on Transportation Data and Modeling|
|EM4S||Organizing committee member, Emerging Mobility Systems and Services Seminar|
|IEEE||International Conference on Intelligent Transportation Systems (ITSC)|
|INFORMS||Session chair, INFORMS Annual Meeting|
|TRB||Transportation Research Board Annual Meeting|
Broader Impacts & Diversity
Second place in OR What? INFORMS Student Video Competition.
No more hunting for parking? Proposed app could do it for you
qluo2 (at) Clemson.edu
Qi Luo 罗琪
277 B Freeman Hall
Department of Industrial Engineering
Clemson, SC 29634