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Longbin Lai

Staff Engineer

Tongyi Lab, Alibaba Group

Biography

Dr. Longbin Lai obtained his Bachelor’s and Master’s degrees from Shanghai Jiao Tong University (SJTU) in 2010 and 2013, respectively. He then pursued his Ph.D. studies in the Database Group at the School of Computer Science and Engineering (CSE), University of New South Wales (UNSW), Sydney, under the supervision of Prof. Xuemin Lin and Prof. Lu Qin. He completed his Ph.D. in March 2017, and his thesis is available here. Dr. Lai joined Alibaba to develop large-scale graph data analytics systems for its e-commerce platform. He is also a lead contributor to the open-source project GraphScope. His work includes GAIA, a distributed dataflow system for large-scale graph queries; GLogS, a distributed interactive pattern matching system; and GOpt, a unified graph query optimization framework. Currently, he is a member of Alibaba Tongyi Lab, where he leads research and development initiatives focusing on innovative applications of Large Language Models (LLMs).

Interests

  • Big Data Management
  • Graph Database
  • Distributed Processing
  • Query Optimizations
  • Large Language Models

Education

  • 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

Projects

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A Graph-Native Query Optimization Framework

GOpt is built on top of a unified intermediate representation (IR) that is capable of capturing both graph and relational operations, …

A High-Throughput Transactional Graph Database

GraphScope Interactive is a specialized construction of GraphScope Flex, designed to handle concurrent graph queries at an impressive …

Interactive Analysis on Distributed Graphs Using Gremlin Language

GAIA (former name, Pegasus) is a distributed data-parallel compute engine based on the cyclic dataflow computation model. GAIA serves …

Interactive Graph Pattern Matching

We develop GLogS system, allowing users to interactively submit queries using a declarative language, which will be compiled and …

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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A Graph-native Optimization Framework for Complex Graph Queries

This technical report extends the SIGMOD 2025 paper – A Modular Graph-Native Query Optimization Framework – by providing a …

A Modular Graph-Native Query Optimization Framework

Complex Graph Patterns (CGPs), which combine pattern matching with relational operations, are widely used in real-world applications. …

Revisiting Graph Analytics Benchmark

The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform …

HINSCAN: Efficient Structural Graph Clustering over Heterogeneous Information Networks

Structural graph clustering (SCAN) is one of the most popular graph clustering paradigms, and has attracted plenty of attention …

Most Probable Maximum Weighted Butterfly Search

Uncertain butterflies are fundamental and popular graphlet motifs within uncertain bipartite networks, serving as a crucial metric in …

Contact

  • 969 Wen Yi Xi Road, Hangzhou, Zhejiang 311121