Qiulin Lin

Research Associate

School of Data Science
City University of Hong Kong
Email: Qiulin.Lin at cityu.edu.hk

Qiulin Lin is currently a Research Associate at School of Data Science, City University of Hong Kong. He received the Ph.D. degree in Information Engineering from The Chinese University of Hong Kong (CUHK) in 2020, advised by Prof. Minghua Chen. He received B.Eng. degree in Electronic Engineering and Information Science from University of Science and Technology of China (USTC) in 2016.

Research Interests

My current research interests include online optimization and algorithms, smart power grid optimization (e.g., energy storagae system management and EV charging), and intelligent transportation systems (e.g. ride-sharing and heavy-duty truck routing).

Projects

Competitive Online Optimization

Online optimization handles optimization problems with sequentially revealed information. One has to make online decisions based on the revealed information only, whereas the classical optimization assumes complete information. We propose a novel and general framework CR-Pursuit [SIGMETRICS'19, SIGMETRICS'22] for online optimization under inventory (budget) constraints and show it achieves close performance to the one with complete information. We have applied our approach to various scenarios, including EV charging [INFOCOM'19, T-ITS'21] and peak-demand reduction [INFOCOM'21 (Poster), ACM e-Energy'21]. Our approach also raises the interests of other researchers and is shown useful in cloud resource provisioning, virtual network functions development, etc. We are interested to see broader applications of our approach and online optimization.

Demand-Aware Ride-Sharing Optimization

Modern ride-sharing systems like Didi Pingche and Uberpool offer an economical and eco-friendly trip mode. Optimizing a ride-sharing system is challenging as it needs to handle tremendous dynamic travel demands in real-time. Meanwhile, the massive amount of data it provides facilitates predicting future demand information. We are interested to see how to apply the predictive demand information to improve the performance of ride-sharing systems. We begin by proposing a new routing paradigm incorporating future demand statistics to optimize the probability and benefit of forming a ride-sharing group for every trip [INFOCOM'18]. We then jointly optimize order dispatching and vehicle routing at system level by taking future demand as a probabilistic resource that can be allocated among drivers [Mobihoc'19]. We show that our approach substantially improves the ride-sharing trip formation and the total profit. Our probabilistic approach is general and can go beyond the ride-sharing scenario to broader application domains.

Publications

Journal
  1. Minimizing Cost-plus-Dissatisfaction in Online EV Charging under Real-Time Pricing
    IEEE Transactions on Intelligent Transportation Systems, Aug. 2022
    Q. Lin*, H. Yi*, and M. Chen
  2. 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, Y. Mo, J. Su, and M. Chen
  3. Competitive Online Optimization under Inventory Constraints
    Proc. ACM Meas. Anal. Comput. Syst., March 2019. (Also in ACM SIGMETRICS/IFIP Performance 2019)
    Q. Lin*, H. Yi*, J. Pang, M. Chen, A. Wierman, M. Honig, and Y. Xiao
Conference
  1. Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach
    ACM SIGMETRICS/IFIP Performance 2022
    Q. Lin, Y. Mo, J. Su, and M. Chen
  2. Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage
    ACM e-Energy 2021
    Y. Mo, Q. Lin, M. Chen, and J. Qin
  3. 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
  4. A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization
    ACM MobiHoc 2019
    Q. Lin, W. Xu, M. Chen and X. Lin,
  5. Competitive Online Optimization under Inventory Constraints
    ACM SIGMETRICS/IFIP Performance 2019
    Q. Lin*, H. Yi*, J. Pang, M. Chen, A. Wierman, M. Honig, and Y. Xiao
  6. Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing
    IEEE INFOCOM 2019
    H. Yi*, Q. Lin*, and M. Chen
  7. Optimal Demand-Aware Ride-Sharing Routing
    IEEE INFOCOM 2018
    Q. Lin, L. Deng, J. Sun and M. Chen,
(*co-primary authors)
Plain Academic