Xiaolin Han, Ph.D.

School of Computer Science
The Northwestern Polytechnical University

Address: No. 1, Dongxiang Road, Chang'an District, Xi'an

Email: xiaolinh (at) nwpu.edu.cn

Biography

I joined the Northwestern Polytechnical University (NWPU) as an associate professor at MIIT Key Laboratory of big data storage and management in November 2022. I was a Postdoctoral Fellow at the University of Hong Kong, working with Prof. Reynold Cheng. I received my Ph.D. degree from the Department of Computer Science at the University of Hong Kong (HKU) in January of 2022, and received M.Eng. degree from Waseda University, and B.Eng. degree from Sichuan University, respectively.

My research interests mainly focus on the areas of data mining and machine learning, particularly spatial-temporal data mining, graph data mining and deep neural networks. Till now, I have published more than 10 papers in the areas of data mining and database, and most of them were published in top-tier conferences and journals (e.g., PVLDB, ICDE, and SIGMOD). I have served as a reviewer for several top conferences and journals (e.g., CIKM、SIGMOD、SIGIR、Information Sciences, Knowledge and Information Systems, Frontiers of Computer Science, Data Science and Engineering(DSE), and Knowledge-Based Systems(KBS)).

Research Interests

My general research areas are spatial-temporal data mining and machine learning. Currently, I am working on the following research topics:

  • Spatial-Temporal Data Mining/Urban Computing: trajectory outlier detection, traffic incident detection
  • Graph Data Mining/Network Representation: knowledge graph embedding, densest subgraph discovery
  • Deep Neural Networks/Graph Neural Networks: graph weight completion, deep generative modeling

Working and Education Experience

  • 2022.2-2022.11: Postdoctoral Fellow, The University of Hong Kong, Hong Kong SAR (China)
  • 2018.2-2022.1: Ph.D., The University of Hong Kong, Hong Kong SAR (China)

Publications(* indicates corresponding authors)

  • FDM: Effective and Efficient Incident Detection on Sparse Trajectory Data,
    Xiaolin Han, Tobias Grubenmann, Chenhao Ma, Xiaodong Li, Wenya Sun, Sze Chun Wong, Xuequn Shang, Reynold Cheng.
    In Information Systems 2024. (CCF-B).
  • Accelerating Directed Densest Subgraph Queries with Software and Hardware Approaches,
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V.S. Lakshmanan, Xiaolin Han*, and Xiaodong Li.
    In VLDBJ 2023. (CCF-A).
  • Finding Locally Densest Subgraphs: A Convex Programming Approach,
    Chenhao Ma, Reynold Cheng, Laks V.S. Lakshmanan, Xiaolin Han*.
    In PVLDB 2022. (CCF-A).
  • DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection,
    Xiaolin Han, Reynold Cheng, Chenhao Ma, Tobias Grubenmann.
    In PVLDB 2022. (CCF-A).
  • Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks,
    Xiaolin Han, Reynold Cheng, Tobias Grubenmann, Silviu Maniu, Chenhao Ma, Xiaodong Li.
    In SIAM SDM 2022. (CCF-B).
  • A Framework for Differentially-Private Knowledge Graph Embeddings,
    Xiaolin Han, Daniele Dell'Aglio, Tobias Grubenmann, Reynold Cheng, Abraham Bernstein.
    In Journal of Web Semantics 2022. (CCF-B).
  • A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery,
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks Lakshmanan, and Xiaolin Han.
    In SIGMOD 2022. (CCF-A).
  • Traffic Incident Detection: A Trajectory-based Approach,
    Xiaolin Han, Tobias Grubenmann, Reynold Cheng, Sze Chun Wong, Xiaodong Li, Wenya Sun.
    In IEEE ICDE 2020. (CCF-A).
  • M-Cypher: A GQL Framework Supporting Motifs,
    Xiaodong Li, Reynold Cheng, Matin Najafi, Kevin Chen-Chuan Chang, Xiaolin Han, Hongtai Cao.
    In CIKM 2020. (CCF-B).
  • An ML-Powered Human Behavior Management System,
    Sihem Amer-Yahia, Reynold Cheng, Mohamed Bouadi, Abdelouahab Chibah, Mohammadreza Esfandiari, Jiangping Zhou, Nan Zhang, Eric Lau, Yuguo Li, Xiaolin Han and others.
    In Bulletin of the Technical Committee on Data Engineering 2020.
  • Traffic Incident Detection: A Deep Learning Framework,
    Xiaolin Han.
    In MDM 2019. (CCF-C).

Selected Honors and Awards

  • HKU Postgraduate Scholarship, HKU, 2018 - 2022
  • IEEE MDM Student Travel Award, 2019
  • Azusa Ono Memorial Scholarship, Waseda University, 2014
  • Honors Scholarship for Privately Financed International Students, Waseda University, 2013
  • Outstanding student of Sichuan University, Sichuan University, 2011

Academic Services

  • Guest Editor: Applied Sciences 2023
  • Guest Associate Editor: Frontiers in Big Data 2023
  • Reviewers/ External reviewers: Transactions on Knowledge and Data Engineering(TKDE), Information Sciences, Knowledge and Information Systems, Frontiers of Computer Science, Data Science and Engineering(DSE), Knowledge-Based Systems(KBS), Mathematics, Chinese Journal of Computers, ICDE2023, PVLDB 2023, CIKM2022, PVLDB 2022, ICDE 2021, KDD 2021, PVLDB 2020, KDD 2020, DASFAA 2020, SSTD 2019, KDD 2019, CIKM 2018
  • Program Committee: ACM CIKM 2023/2024 Research track, IEEE BigData 2024 Research track, ACM SIGMOD 2023 Availability and Reproducibility Committee, SIGIR 2023/2024 Resource paper track, IEA-AIE 2023 Research track

Academic Talks

  • Traffic Incident Detection: A Deep Learning Framework. IEEE MDM 2019, Hong Kong, China, June 10, 2019
  • Traffic Incident Detection: A Trajectory-based Approach. ICDE 2020, Texas, America, April 20, 2020 (virtual conference)
  • Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks. SIAM SDM, Virginia, U.S., April 28, 2022 (virtual conference)
  • DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection. Sydney, Australia, September 7, 2022 (virtual conference)

Teaching Experience

  • The Age of Big Data, 2019 Spring, 2020 Spring, 2022 Spring, Teaching Assistant
  • Advanced Database Systems, 2020 Fall, Teaching Assistant
  • Big Data and Data Mining, 2021 Fall, Teaching Assistant