Shuhan Yuan

Anomaly Detection Techniques and Applications Network Security and Intrusion Detection Topic Modeling Software System Performance and Reliability Imbalanced Data Classification Techniques

Publications

Citations

2,229

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2013

Research summary

AI-generated

Shuhan Yuan works in data mining and machine learning, with a focus on anomaly detection—automatically spotting unusual or suspicious patterns in data such as system logs, financial transactions, and time-series measurements. Recent work explores making these detection systems more explainable and fair, diagnosing the root causes of faults, and studying security weaknesses like 'backdoor attacks' that can be planted in detection models. Some projects also touch on fairness and bias in AI systems that generate images from text.

Anomaly and fraud detectionExplainable and fair machine learningSecurity of detection models (backdoor attacks)Causal inference for fault diagnosisLog analysis and time-series data mining

Publication activity has been steady with year-to-year fluctuation, peaking around 2019-2023 and averaging roughly 8 papers per year over the last five years.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 7.8/year recently

Recent publications

A Causal Inference Framework for Actionable Fault Diagnosis and Mitigation
2026
Few-shot anomaly detection and classification through reinforced data selection with a combinatorial reward
Knowledge and Information Systems · 2025
Backdoor Attack against Log Anomaly Detection Models
2025
Differences Breed Creativity: Connecting Quality of Parent-Child Interaction to Workplace Creativity
Academy of Management Proceedings · 2025
BiasMap: Leveraging Cross-Attentions to Discover and Mitigate Hidden Social Biases in Text-to-Image Generation
arXiv (Cornell University) · 2025
Contrastive Learning for Fraud Detection from Noisy Labels
2024
Backdoor Attack Against One-Class Sequential Anomaly Detection Models
Lecture notes in computer science · 2024
Backdoor Attack against One-Class Sequential Anomaly Detection Models
arXiv (Cornell University) · 2024
Achieving Counterfactual Explanation for Sequence Anomaly Detection
Lecture notes in computer science · 2024
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection
arXiv (Cornell University) · 2024
How ecological policy stringency moderates the influence of industrial innovation on environmental sustainability: The role of renewable energy transition in BRICST countries
Renewable Energy · 2023
LogGPT: Log Anomaly Detection via GPT
2023
On Root Cause Localization and Anomaly Mitigation through Causal Inference
2023
Explainable Sequential Anomaly Detection via Prototypes
2023
Achieving Counterfactual Fairness for Anomaly Detection
Lecture notes in computer science · 2023

Publishes in

arXiv (Cornell University)×21
Lecture notes in computer science×9
2021 IEEE International Conference on Big Data (Big Data)×3
Proceedings of the AAAI Conference on Artificial Intelligence×2
2022 IEEE International Conference on Big Data (Big Data)×2

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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