Research
My research program is dedicated to addressing online optimization problems where information is revealed sequentially. I strive to develop theoretically sound and practically impactful algorithms that can adapt to uncertainty and improve system performance in real-world scenarios, particularly within AI-driven and cyber-physical infrastructures.
Competitive Online Optimization
This research thrust focuses on developing robust frameworks for online optimization problems constrained by inventories or budgets. Our novel CR-Pursuit framework demonstrates performance close to offline optimal solutions, showcasing its efficacy in diverse applications such as EV charging, peak-demand reduction, and IoT device energy management. We are actively exploring broader applications and theoretical extensions of this work.
Key Publications:
- Q. Lin, J. Su, and M. Chen. "Competitive Online Age-of-Information Optimization for Energy Harvesting Systems." IEEE INFOCOM, 2024.
- Q. Lin, Y. Mo, J. Su, and M. Chen. "Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach." Proc. ACM Meas. Anal. Comput. Syst., June 2022 (Also in ACM SIGMETRICS/IFIP Performance 2022).
- Q. Lin*, H. Yi*, J. Pang, M. Chen, A. Wierman, M. Honig, and Y. Xiao. "Competitive Online Optimization under Inventory Constraints." Proc. ACM Meas. Anal. Comput. Syst., March 2019. (Also in ACM SIGMETRICS/IFIP Performance 2019).
- H. Yi*, Q. Lin*, and M. Chen. "Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing." IEEE INFOCOM 2019.
- Y. Mo, Q. Lin, M. Chen, and J. Qin. "Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage." ACM e-Energy 2021.
Smart Power Grid Optimization
My work in this area focuses on optimizing operations within smart power grids, particularly concerning energy storage system management, efficient electric vehicle (EV) charging strategies, and peak demand reduction. These include developing algorithms that can handle real-time pricing and user dissatisfaction for EV charging, and optimizing large-load user energy storage to minimize peak demands, contributing to grid stability and cost-efficiency.
Key Publications:
- Q. Lin*, H. Yi*, and M. Chen. "Minimizing Cost-plus-Dissatisfaction in Online EV Charging under Real-Time Pricing."" IEEE Transactions on Intelligent Transportation Systems, Aug. 2022.
- H. Yi*, Q. Lin*, and M. Chen. "Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing." IEEE INFOCOM 2019.
- Y. Mo, Q. Lin, M. Chen, and J. Qin. "Optimal Peak-Minimizing Online Algorithms for Large-Load Users with Energy Storage." IEEE INFOCOM 2021 (poster paper, Best Poster Award).
- Y. Mo, Q. Lin, M. Chen, and J. Qin. "Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage." ACM e-Energy 2021.
Intelligent Transportation Systems
Modern ride-sharing platforms present significant optimization challenges due to dynamic demand and real-time operational constraints. My research in this area leverages predictive demand information to enhance system efficiency. We have proposed novel routing paradigms and joint optimization strategies for order dispatching and vehicle routing that demonstrably improve ride-sharing trip formation and overall profitability. This probabilistic approach has potential applications beyond ride-sharing. My research in intelligent transportation systems extends to optimizing logistics for heavy-duty electric trucks, focusing on minimizing carbon footprint while ensuring timely deliveries. This involves developing approximation algorithms for complex routing problems that consider factors like renewable energy availability ("follow the sun and go with the wind").
Key Publications:
- Q. Lin, W. Xu, M. Chen and X. Lin. "A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization." ACM MobiHoc 2019.
- Q. Lin, L. Deng, J. Sun and M. Chen. "Optimal Demand-Aware Ride-Sharing Routing." IEEE INFOCOM 2018.
- J. Su, Q. Lin, M. Chen, and H. Zeng. "Minimizing Carbon Footprint for Timely E-Truck Transportation: Hardness and Approximation Algorithm." IEEE CDC, 2023 (invited).
- J. Su, Q. Lin, and M. Chen. "Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation." ACM e-Energy 2023 (Best Paper Award).