Prof. Tsz Nam Chan (Edison)

Distinguished Professor

College of Computer Science and Software Engineering
Shenzhen University
Office: Zhiteng Building 618-3
my email: edisonchan2013928@gmail.com or edisonchan@szu.edu.cn
Remember: "Try our best and prepare for the worst"

Biography

Tsz Nam Chan (Edison) is currently a Distinguished Professor in the database group of the Big Data Institute in Shenzhen University (SZU). He is a data engineering researcher (for handling the efficiency issues in big data settings). He published several research papers in prestigious conferences and journals (CCF: A, top ranking in Google scholar and Microsoft) in both database (data engineering) and data mining areas, including SIGMOD, VLDB, ICDE, SIGKDD, and TKDE. Prior to joining the SZU, he was a Research Assistant Professor in the Hong Kong Baptist University from Sep 2020 to Aug 2023 and a postdoctoral researcher in the The University of Hong Kong from Sep 2018 to Aug 2020. He received the PhD degree in computing and the BEng degree in electronic and information engineering from The Hong Kong Polytechnic University in 2019 and 2014, respectively. He is an IEEE senior member, an ACM member, and an AAAI member.

Major Research-Oriented Awards
  • IEEE Senior Member (Awarded in 2024)
  • National Science Fund for Excellent Young Scientists (Overseas) (国家海外优青) (Awarded in 2023)
Recruitment

Tsz Nam Chan can recruit an assistant professor (subject to the final decision from SZU), an associate researcher (副研究员), postdoctoral researchers, postgraduate students (including master and PhD students), and software developers. If you are interested in developing efficient software packages/systems (like LIBKDV and KDV-Explorer) or complexity-reduced algorithms (like SLAM, SWS, SAFE, and PLAME) that are related to spatial/multi-dimensional databases, GIS, and statistical models, please contact me.

To all potential students: Suppose that you want to join our research group (https://szu-dbgroup.github.io/) as a PhD student/MSc student. You need to send me your CV and your transcript. Note that our research group (1) mainly focuses on algorithm development with non-trivial guarantees (e.g., time complexity reduction/accuracy guarantees) for spatiotemporal data management/graph data management, (2) aims for publishing papers in top-tier venues (e.g., SIGMOD, VLDB, ICDE, SIGKDD, TODS, VLDBJ, and TKDE), and (3) needs highly motivated students. Therefore, those students who do not fulfill the above three conditions should not join this group.

To all potential postdoctoral researchers/associate researchers: If you want to join our research group (https://szu-dbgroup.github.io/) as a postdoctoral researcher/associate researcher, you (1) must publish some papers in top-tier venues (e.g., SIGMOD, VLDB, ICDE, SIGKDD, TODS, VLDBJ, and TKDE) before as a first author and (2) must work on database/data mining/visualization/theoretical computer science areas. Those applicants who mainly want to apply machine learning models to improve the accuracy of some tasks should not apply for the postdoctoral researcher/associate researcher position in our group.

Research Interests

  • Algorithm and software development for statistical models (with non-trivial accuracy and time complexity guarantees)

  • Spatial and temporal data analysis/management (develop efficient algorithms for GIS)

  • Large-scale data visualization

  • Kernel methods, similarity measures, similarity search, and pattern matching
    		•

    Long-term Goal

    My long-term goal aims to develop the GIS (or spatial analysis), visualization, statistical, and machine learning software packages, like QGIS, ArcGIS, CrimeStat, Seaborn, Scikit-learn, Scipy, and LIBSVM, which are based on our theoretically efficient algorithms (e.g., reduce the time complexity) with non-trivial accuracy guarantees (e.g., achieve exact results or approximate results with approximation ratio). With the lower time complexity of our solutions, our software packages should be the fastest in the world.

    Academic Talks
    Research Grants
    • PI: Shenzhen University Internal Grant 2024 (研究生优秀教材建设项目) "如何调整心态去撰写计算机科学论文？" (How to Change Your Mindset to Write Academic Papers in Computer Science?), 200,000 RMB
    • PI: Start-up grant from Shenzhen City (B-grade) 2024 (孔雀计划科研启动经费B档) "快速地理信息系统算法" (Fast Algorithms for Geographic Information Systems), 5,000,000 RMB
    • PI: SZU Additional Start-up Grant 2024 "大时空数据可视化" (Large-scale Spatiotemporal Data Visualization), 700,000 RMB
    • PI: NSFC (Excellent Young Scientists (Overseas)) 2023 "地理时空大数据分析" (Large-scale Spatiotemporal Data Analytics), 1,000,000 - 3,000,000 RMB
    • PI: SZU Start-up Grant 2023 "快速密度估计算法" (Fast Algorithms for Density Estimation), 300,000 RMB
    • PI: NSFC 2022 "基于超快速算法的核密度估计" (Efficient Algorithms for Kernel Density Estimation), 300,000 RMB
    • PI: HKBU Internal Grant, 100,000 HKD
    • PI: HKBU Start-up Grant, 120,000 HKD
    Research Students and Research Assistants
    • Hongwei Ye, master student in SZU (Cosupervised with Prof. Joshua Zhexue Huang)
    • Yue Zhong, master student in SZU (Cosupervised with Prof. Joshua Zhexue Huang)
    • Bojian Zhu, undergraduate research assistant in HKBU (From June 2023 to August 2024) (Coauthored two VLDB papers with me)
    • Rui Zang, undergraduate research assistant in HKBU (From May 2023 to July 2023) (Coauthored one VLDB paper and one SIGMOD demo paper with me)
    Software/System Development
    • Fast Line Density Analysis: A fast QGIS plug-in for supporting Line Density Visualization.

    • Fast Density Analysis: A fast QGIS plug-in for supporting Kernel Density Visualization, Network Kernel Density Visualization, and Spatial-temporal Kernel Density Visualization.

    • Rlibkdv: A fast R library for supporting Kernel Density Visualization, Network Kernel Density Visualization, and Spatial-temporal Kernel Density Visualization.

    • PyNKDV: A fast python library for supporting Network Kernel Density Visualization. (Accepted in SIGMOD 2023, demo track)

    • LIBKDV: A fast python library for supporting Kernel Density Visualization and Spatial-temporal Kernel Density Visualization. (Accepted in VLDB 2022, demo track)

    • KDV-Explorer: A fast online system for supporting Kernel Density Visualization and Spatial-temporal Kernel Density Visualization. (Accepted in VLDB 2021, demo track)
    Research Publications [DBLP][Google Scholar]
    Demo Publications
    Tutorials
    Professional Services
    • Major Service Award
      • Outstanding Reviewer Award in IEEE International Conference on Data Engineering (ICDE) 2024
    • Journal Referee
      • The International Journal on Very Large Data Bases (VLDBJ)
      • IEEE Transactions on Knowledge and Data Engineering (TKDE)
      • Artificial Intelligence Journal (AIJ)
      • IEEE Transactions on Computers (TC)
      • World Wide Web Journal (WWWJ)
      • ACM Transactions on Spatial Algorithms and Systems (TSAS)
      • SoftwareX Journal
      • IEEE Transactions on Network Science and Engineering (TNSE)
      • Pattern Recognition (PR)
      • Data and Knowledge Engineering (DKE)
      • Digital Signal Processing (DSP)
      • Journal of Computer Science and Technology (JCST)
      • The Journal of Supercomputing
      • Cities Journal
      • Remote Sensing Journal
      • Engineering Applications of Artificial Intelligence Journal
      • Sensors Journal
      • Entropy Journal
    • Conference Program Organizer
      • International Conference on Mobile Data Management (MDM) Year: 2021 to 2025 proceedings chair
    • Conference Program Committee/Reviewer
      • International Conference on Very Large Data Bases (VLDB) Year: 2022 to 2024 (demo), 2025 (research)
      • IEEE International Conference on Data Engineering (ICDE) Year: 2022, 2024, 2025
      • Proceedings of International Conference on Extending Database Technology (EDBT) Year: 2023
      • Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Year: 2024, 2025
      • International Joint Conference on Artificial Intelligence (IJCAI) Year: 2020
      • International Conference on Scientific and Statistical Database Management (SSDBM) Year: 2024
      • Asian Conference on Machine Learning (ACML) Year: 2024
      • International Conference on Database Systems for Advanced Applications (DASFAA) Year: 2021 to 2024
      • International Conference on Web Information Systems Engineering (WISE) Year: 2019 to 2024
    • Conference Program Session Chair
      • International Conference on Very Large Data Bases (VLDB) Year: 2020, 2022
      • IEEE International Conference on Data Engineering (ICDE) Year: 2021, 2024
      • International Joint Conference on Web and Big Data (APWeb-WAIM) Year: 2021
      • International Conference on Web Information Systems Engineering (WISE) Year: 2021
    Teaching
    • SZU Professional English (for PhD Students) (Spring 2024)
    • SZU Software Testing (Spring 2024)
    • HKBU COMP 7640 Database Systems and Administration (Spring 2023)
    • HKBU COMP 7930 Big Data Analytics (Spring 2021, Spring 2022)
    • HKBU COMP 4035 Database System Implementation (Fall 2020, Fall 2021, Fall 2022)
    How to be Productive in Research?

    If you want to be productive in research, you need to read this document (by Prof. Dimitris Papadias in HKUST), read this Zhihu blog (written in Chinese), and watch this video (by Prof. Baochun Li in the University of Toronto).