Skip to content

Hi there 👋

The Large-scale Data Algorithms and AI Systems (LAGAS) Group develops efficient algorithms, models, and systems for managing, analysing, and accessing large-scale, heterogeneous data. Our research spans data analytics and mining, information retrieval and recommendation, and efficient AI learning and inference. We work with graph, relational, vector, textual, and behavioural data, with applications extending to biological and chemical data analysis.

Our work encompasses algorithm design, AI model development, and their integration, including approaches built on machine learning and large language models. We focus on computational efficiency and scalability, studying the trade-offs between computation, memory use, latency, and solution quality. Our goal is to make complex data easier to process, understand, and use, supporting intelligent applications and data-driven scientific discovery.

Members

Dr. Renchi YANG
Dr. Renchi YANG

Principal Investigator
Mr. Hongtao WANG
Mr. Hongtao WANG

PhD Student
Mr. Yurui LAI
Mr. Yurui LAI

PhD Student
Mr. Xiaoyang LIN
Ms Xiaoyang LIN

PhD Student
Mr. Runhao JIANG
Mr. Runhao JIANG

PhD Student
Mr. Taiyan ZHANG
Mr. Taiyan ZHANG

PhD Student
Mr. Yixi Zhou
Mr. Yixi ZHOU

PhD Student
Mr. Donghao WU
Mr. Donghao WU

PhD Student
Mr. Rongguang LIANG
Mr. Rongguang LIANG

PhD Student

Popular repositories Loading

  1. Awesome-Item-ID-Gen-RecSys Awesome-Item-ID-Gen-RecSys Public

    Updating curated list of research advancements on item identification and item tokenization in generative recommender systems. The survey is titled "A Survey of Item Identifiers in Generative Recom…

    120 4

  2. Awesome-Graph-Datasets Awesome-Graph-Datasets Public

    A curated list of graph datasets of various types, including plaingraphs, attributed graphs, bipartite graphs, text-attributed graphs, multi-modal graphs, temporal graphs, etc.

    12

  3. HOPE HOPE Public

    SIGMOD 2024 paper titled "Efficient High-Quality Clustering for Large Bipartite Graphs"

    Python 7 6

  4. TADA TADA Public

    the official implementation of KDD2024 paper "Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks"

    Python 6 3

  5. Locle Locle Public

    Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs

    Python 6 1

  6. TPC TPC Public

    The official implementation of the KDD 2024 paper "Effective Clustering on Large Attributed Bipartite Graphs"

    Python 5 1

Repositories

Showing 10 of 25 repositories

Top languages

Loading…

Most used topics

Loading…