Xiangxiang DAIDepartment of Computer Science and Engineering,The Chinese University of Hong Kong (CUHK) Email: xiangxdai0@gmail.com Google Scholar Profile |
About me
I am a new Ph.D. student in Advanced Networking and System Research Laboratory (ANSRLab) supervised by Chair Professor John C.S. Lui (ACM/IEEE Fellow) at the Department of Computer Science & Engineering, The Chinese University of Hong Kong (CUHK). Before that, I obtained my bachelor’s degree (Advanced Class, GPA: 3.95/4.0) from the Department of Electronics and Information Engineering at Huazhong University of Science and Technology (HUST). I am interested in reinforcement learning and bandit theory and their algorithm design for various applications, such as web recommendation systems, video analytics, computer networks, and large language models. I am always open to possible collaborations. Please feel free to contact me.
Publication
(* Equal Contribution)
- Xiangxiang Dai*, Zhiyong Wang*, Jiancheng Ye, and John C.S. Lui. Quantifying the Merits of Network-Assist Online Learning in Optimizing Network Protocols. Accepted by the IEEE/ACM International Symposium on Quality of Service (IWQoS), 2024. (81/326 = 24.8%, CCF B)
- Xiangxiang Dai, Peng Yang, Xinyu Zhang, Zhewei Dai, and Li Yu. RESPIRE: Reducing Spatial–Temporal Redundancy for Efficient Edge-Based Industrial Video Analytics. IEEE Transactions on Industrial Informatics (TII), vol. 18, no. 12, pp. 9324–9334, 2022. (SCI Q1)
Selected Awards
- 2023-2027, CUHK Vice-Chancellor’s PhD Scholarship
(Only 30 PhD students in the whole university of CUHK) - 2022, Pacemaker to Merit Student
(Highest honor in HUST, awarded to 20 students from all undergraduate grades, selected from National Scholarship recipients across the university, ≈0.07%) - 2023, Honour Bachelor
(Highest honor for graduates in HUST on academic performance, 199/6500≈3%) - 2022, National Scholarship
- 2023, Xiaomi Special First-Class Scholarship (With an invitation to visit Xiaomi Headquarters)
Services
- Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering (TKDE, CCF A), IEEE Internet of Things Journal (IoT, SCI Q1).