Xintao Wu
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor
Also affiliated: South China Agricultural University (2011); University of North Carolina at Charlotte (2002–2014); University of Vermont (2016); North Carolina State University (2005); Baylor University (2026); George Mason University (1999–2001); Hefei University of Technology (2015); University of Oregon (2019); University of Arkansas System (2017–2026); Beijing Chaoyang Emergency Medical Center (2024); East China Normal University (2025); Beijing University of Chemical Technology (2023–2024); Zhejiang University (2024–2025); Shanghai Maritime University (2024)
Faculty Researcher
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Xintao Wu's research focuses on developing and applying advanced computational methods, particularly in machine learning and artificial intelligence, to address complex challenges in areas such as fairness, privacy, and medical imaging. His work has explored the creation of pre-trained language models for specialized domains, including mathematics education and general natural language processing tasks.
Wu has received federal funding from the National Science Foundation (NSF) for research aimed at achieving counterfactually fair machine learning through causal modeling and for investigating fair regression under sample selection bias. His publications include studies on log anomaly detection, insider threat detection, and the empirical analysis of large visual language models for medical imaging. He also co-authored work on statistical and causal fairness, providing frameworks for understanding and mitigating disparate impacts in machine learning models.
With a scholarly output reflected in an h-index of 41 and over 6,000 citations across more than 350 publications, Wu is recognized as a highly cited researcher. He maintains an active research group at the University of Arkansas at Fayetteville, collaborating with colleagues on various projects. His research network includes consistent collaborations with Minh-Hao Van, Alycia N. Carey, Aneesh Komanduri, and Prateek Verma.
Metrics
- h-index: 41
- Publications: 354
- Citations: 6,219
Selected Publications
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StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers (2026)arXiv (Cornell University) OpenAlex
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Leveraging Foundation Models for Causal Generative Modeling (2026)
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How Does Differential Privacy Affect Social Bias in LLMs? A Systematic Evaluation (2026)
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Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges (2026)
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Class-Domain Incremental Learning on Graphs via Disentangled Knowledge Distillation (2026)
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AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System (2026)
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Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models (2026)
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LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (2026)
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Achieving Distributive Justice in Federated Learning via Uncertainty Quantification (2026)
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LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (2026)
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A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools (2026)
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A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks (2026)
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A Machine Learning Framework for Automated Computational Ethology Using Markerless Pose Estimation (2025)
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Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction (2025)
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A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application (2025)
Federal Grants 2 $634,828 total
III:Small: Counterfactually Fair Machine Learning through Causal Modeling
Collaboration Network
Top Collaborators
- LogBERT: Log Anomaly Detection via BERT
- Deep learning for insider threat detection: Review, challenges and opportunities
- Contrastive Learning for Insider Threat Detection
- Hidden Buyer Identification in Darknet Markets via Dirichlet Hawkes Process
- Using Dirichlet Marked Hawkes Processes for Insider Threat Detection
Showing 5 of 10 shared publications
- Fairness-aware Agnostic Federated Learning
- Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent
- Fair Data Generation and Machine Learning Through Generative Adversarial Networks
- Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
- Achieving Counterfactual Explanation for Sequence Anomaly Detection
- Achieving Counterfactual Fairness for Causal Bandit
- Achieving User-Side Fairness in Contextual Bandits
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Fairness-aware Bandit-based Recommendation
- Transferable Contextual Bandits with Prior Observations
- Fairness-aware Agnostic Federated Learning
- Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent
- Fair and Robust Classification Under Sample Selection Bias
- Robust Personalized Federated Learning under Demographic Fairness Heterogeneity
- Robust Fairness-aware Learning Under Sample Selection Bias
- Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
- Towards Fair Disentangled Online Learning for Changing Environments
- Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
- Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
- Class-Domain Incremental Learning on Graphs via Disentangled Knowledge Distillation
- Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT
- MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT
- MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Fairness-aware Agnostic Federated Learning
- InfoFair: Information-Theoretic Intersectional Fairness
- Attent: Active Attributed Network Alignment
- Fair Regression under Sample Selection Bias
- Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- A Generative Adversarial Framework for Bounding Confounded Causal Effects
- Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition
- Poisoning Attacks on Fair Machine Learning
- Fair In-Context Learning via Latent Concept Variables
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
- A Machine Learning Framework for Automated Computational Ethology Using Markerless Pose Estimation
- The statistical fairness field guide: perspectives from social and formal sciences
- The Causal Fairness Field Guide: Perspectives From Social and Formal Sciences
- Robust Personalized Federated Learning under Demographic Fairness Heterogeneity
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
- Towards Fair Disentangled Online Learning for Changing Environments
- Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
- Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
- Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
- Towards Fair Disentangled Online Learning for Changing Environments
- Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
- Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
- Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
- Towards Fair Disentangled Online Learning for Changing Environments
- Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
- Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
- On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
- Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
- A Machine Learning Framework for Automated Computational Ethology Using Markerless Pose Estimation
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