Xintao Wu Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Federal Grant PI High Impact

Professor

University of Arkansas at Fayetteville

faculty

39 h-index 352 pubs 5,823 cited

Research Areas

Fairness in Machine Learning Causal Inference in Machine Learning Privacy-Preserving Machine Learning Advanced Neural Network Applications Machine Learning Model Auditing Computer Vision in Medical Imaging Natural Language Processing Techniques

Links

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OverviewAI-generated summary

Xintao Wu investigates machine learning, with a particular focus on fairness and privacy in artificial intelligence systems. His research addresses the ethical considerations and technical challenges of developing AI that is both effective and equitable. Wu has received federal funding from the National Science Foundation (NSF) for his work, including a $484,828 grant as Co-PI for a project on counterfactually fair machine learning through causal modeling and a $150,000 grant as PI for research on fair regression under sample selection bias.

His recent publications explore diverse applications and theoretical aspects of machine learning. These include work on log anomaly detection using BERT, deep learning for insider threat detection, fairness-aware federated learning, and the statistical and causal fairness of AI models. Wu's research also extends to analyzing large visual language models for medical imaging and methods for removing disparate impact in differentially private stochastic gradient descent. He maintains an active research group and collaborates with colleagues at the University of Arkansas at Fayetteville, including Wen Huang and Minh-Hao Van.

Wu's scholarly contributions are reflected in his metrics, with an h-index of 39 and over 5,800 citations across more than 350 publications. He is recognized as a high-impact researcher due to his extensive citation record and his role as a principal investigator on federal grants.

Metrics

h-index: 39
Publications: 352
Citations: 5,823

Selected Publications

Privacy Preserving Prompt Engineering: A Survey (2025) DOI
DP-TabICL: In-Context Learning with Differentially Private Tabular Data (2024) DOI
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis (2024) DOI
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models (2024) DOI
Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks (2024) DOI
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously (2024) DOI
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study (2024) DOI
Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition (2024) DOI
Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks (2024) DOI
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach (2024) DOI
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness (2024) DOI
Robust Fraud Detection via Supervised Contrastive Learning (2023) DOI
Towards Fair Disentangled Online Learning for Changing Environments (2023) DOI
InfoFair: Information-Theoretic Intersectional Fairness (2022) DOI
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks (2022) DOI