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陈亮

Liang Chen

@Guangzhou, China.

About me

Dr. Liang Chen is currently an Associate Professor at School of Computer Science and Engineering in Sun Yat-Sen University, Guang Zhou, China. He received a PhD and a Bachelor’s degree from Advanced Computing and System Laboratory (CCNT), College of Compter Science &Technology at Zhejiang University, China, respectively in 2015 and 2009. His research area includes Graph Learning, Trustworthy AI, Large Language Model, and Recommendation System. (Google Scholar)

Position Opening! Looking for Students with Research Passion! (Research Intern, Master, PhD.)

What’s new

  • [NeurIPS-2024 Workshop on Audio Imagination] 2024/10/17, our work on “Parrot: Autoregressive Spoken Dialogue Language Modeling with Decoder-only Transformers” has been accepted.
  • [NeurIPS-2024] 2024/09/26, our work on “State Space Models on Temporal Graphs: A First-Principles Study” has been accepted.
  • [KDD-24] 2024/05/17, our work on “One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes” has been accepted.
  • [KDD-24] 2024/05/17, our collaboration with Ant Group on “Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective” has been accepted.
  • [ACL-24] 2024/05/16, our collaboration with Tencent on “Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm” has been accepted.
  • [ICML-24] 2024/05/02, our collaboration with HKUST and Tencent on “Parameter-Efficient Fine-Tuning with Discrete Fourier Transform” has been accepted.
  • [TCSS] 2024/04/04, our work on “FairAGG: Towards Fair Graph Neural Networks via Fair Aggregation” has been accepted.
  • [TCSS] 2024/3/11, our work on “A Review-level Sentiment Information Enhanced Multi-task Learning Approach for Explainable Recommendation” has been accepted.
  • [WWW-24] 2024/1/23, our work on “Fair Graph Representation Learning via Sensitive Attribute Disentanglement” has been accepted.
  • [ICLR-24] 2024/1/16, our work on “A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks” has been accepted.