Final-year Ph.D. Student · Shanghai University

Junquan Gu 顾峻铨

I work on Automated Machine Learning, LLM-driven feature and model optimization, graph learning, fraud detection, and anomaly detection, with an emphasis on structured data and decision-oriented machine learning systems.

AutoML AutoFE LLM-driven ML Systems Graph Learning Fraud Detection Anomaly Detection

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About

I am a final-year Ph.D. student at the School of Computer Engineering and Science, Shanghai University. My research develops automated learning systems that connect task semantics, feature construction, model selection, optimization, and evaluation for structured-data problems.

Advisors: Hang Yu and Xiangfeng Luo.

Research Focus

LLM-assisted automated feature engineering and model design for tabular data.
Graph-based anomaly and fraud detection under incomplete, imbalanced, and cross-domain settings.
Reliable AutoML pipelines with validation-driven refinement, adaptive ensembling, and calibrated outputs.

Selected Academic Profile

My first-author work spans graph anomaly detection, LLM-driven AutoML, automated feature engineering, and open-source fraud-data simulation. Several manuscripts are currently under review or revision.

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Service & Projects

Reviewer for SIGIR, ICASSP, CAIS, and related venues. During my Ph.D., I have contributed to national-level research projects and coordinated student research on AutoML and fraud detection.

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