Dr. Longbin Lai obtained his Degree of Bachelor and Master from Shanghai Jiao Tong University (SJTU) in 2010 and 2013 respectively. After that, He joined the Database group in CSE, UNSW, Sydney for Ph.D study under the supervision of Prof. Xuemin Lin and Dr. Lu Qin. He obtained his Ph.D degree in Mar. 2017, and his thesis is available here. He now joins Alibaba Damo Academy to build intelligent systems for big (graph) data processing. His research interests include graph algorithms, graph database, database management, distributed algorithms and systems.


  • Big Data Management
  • Graph Database
  • Distributed Processing
  • Query Optimizations


  • PhD in Computer Science and Engineering, 2017

    University of New South Wales, Sydney

  • Master in Computer Engineering, 2013

    Shanghai Jiao Tong University

  • BSc in Information Security, 2010

    Shanghai Jiao Tong University



Distributed Pattern Matching System with Cypher

To develop a graph pattern matching system in the distributed context, while gluing together the academic results of optimal join …

Recent Publications

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Parallelization of Butterfly Counting on Hierarchical Memory

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the …

GraphScope Flex: LEGO-like Graph Computing Stack

Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. …

Bridging the Gap between Relational OLTP and Graph-based OLAP

This paper presents GART, an in-memory system extended from hybrid transactional/analytical processing (HTAP) systems for hybrid …

GLogS: Interactive Graph Pattern Matching Query At Large Scale

Interactive GPM (iGPM) is becoming increasingly important for data scientists to explore graphs in real life, where a series of graph …

FLASH: A Framework for Programming Distributed Graph Processing Algorithms

As a result of decades of studies, a broad spectrum of graph algorithms have been developed for graph analytics, including clustering, …


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