Dr. CHAN, Edison Tsz Nam

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陳梓楠博士
B.Eng., Ph.D.
Research Assistant Professor, Department of Computer Science

https://www.comp.hkbu.edu.hk/~edisonchan/



AboutDr. Tsz Nam Chan (Edison) is currently a research assistant professor in the Hong Kong Baptist University (HKBU). 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 data engineering and data mining area, including SIGMOD, VLDB, ICDE, and TKDE. Prior to joining the HKBU, he worked as the postdoctoral researcher in The University of Hong Kong (HKU) 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 also serves as the program committee members and reviewers of several prestigious conferences and journals, including VLDB (demo), ICDE, IJCAI,DASFAA, WISE, IEEE TKDE, IEEE TC, IEEE TNSE, and PR Journal. In addition, he is also the proceedings chair of IEEE MDM2021 conference and MDM2022 conference.

Research InterestsKernel methods
Similarity measures, similarity search and pattern matching
Spatial and temporal data analysis(develop efficient algorithms for GIS)

Selected PublicationsYun Peng, Byron Choi, Tsz Nam Chan, Jianliang Xu. "LAN: Learning-based Approximate k-Nearest Neighbor Search in Graph Databases
SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization
SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization
SAFE: A Share-and-Aggregate Bandwidth Exploration Framework for Kernel Density Visualization
PAW: Data Partitioning Meets Workload Variance
KDV-Explorer: A Near Real-Time Kernel Density Visualization System for Spatial Analysis
Fast Augmentation Algorithms for Network Kernel Density Visualization
PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries
Detail-Preserving Multi-Exposure Fusion with Edge-Preserving Structural Patch Decomposition
Effective and Efficient Discovery of Top-k Meta Paths in Heterogeneous Information Networks
Efficient Algorithms for Kernel Aggregation Queries
QUAD:Quadratic-Bound-based Kernel Density Visualization
The Power of Bounds:Answering Approximate Earth Mover's Distance with Parametric Bounds
KARL: Fast KernelAggregation Queries
Efficient Sub-WindowNearest Neighbor Search on Matrix
FEXIPRO: Fastand Exact Inner Product Retrieval in Recommender Systems", Proceedings of ACM Conference on Management of Data (SIGMOD), 2017.