I am a Ph.D. student in Computer Science at the University of Illinois Chicago. I am fortunate to be advised by Prof. Philip S. Yu. My research interest is generative models for heterogeneous and multimodal data.

Previously, I received my M.S. in Biomedical Data Science and Mathematics from University of Wisconsin-madison, where I am grateful to have worked with Prof. Yin Li and Prof. Vikas Singh. I received my B.S. in Mathematics from Sun Yat-sen University.

Currently, I am a research intern at Bosch Center for Artificial Intelligence.

Preferred way of being contacted: lfang87 AT uic DOT edu

[Curriculum Vitae]

Publications

• A Robust Invisible Watermark for Tabular Generative Models

  Liancheng Fang, Aiwei Liu, Henry Peng Zou, Hengrui Zhang, Philip S. Yu

  1st Workshop on GenAI Watermarking at ICLR 2025

• TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency

  Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

  Preprint 2025

• Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances

  Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Liancheng Fang, Zhen Wang, Philip S. Yu

  Preprint 2025

• TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation

  Liancheng Fang, Aiwei Liu, Hengrui Zhang, Henry Peng Zou, Weizhi Zhang, Philip S. Yu

  Preprint 2025

  3rd Table Representation Learning Workshop at NeurIPS 2024 (Short Version)

• ClariGen: Bridging Instruction Gaps via Interactive Clarification in Code Generation

  Chunyu Miao, Yibo Wang, Langzhou He, Liancheng Fang, Philip S. Yu

  Preventing and Detecting LLM Misinformation Workshop at AAAI 2025

• Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data

  Hengrui Zhang*, Liancheng Fang*, Qitian Wu, Philip S. Yu

  Preprint 2024

• Can Watermarked LLMs be Identified by Users via Crafted Prompts?

  Aiwei Liu, Sheng Guan, Yiming Liu, Leyi Pan, Yifei Zhang, Liancheng Fang, Lijie Wen, Philip S. Yu, Xuming Hu

  ICLR 2025 Spotlight

• Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

  Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Liancheng Fang, Philip S. Yu

  CIKM 2024

• Unleashing the Potential of Diffusion Models for Incomplete Data Imputation

  Hengrui Zhang, Liancheng Fang, Philip S. Yu

  ICLR 2025 Spotlight

• ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction

  Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea

  ACL 2024 Findings