M
Minh-Hao Van Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
5 h-index
30 pubs
139 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Minh-Hao Van's research focuses on machine learning, particularly in areas related to robustness and fairness. His work investigates methods to defend against adversarial attacks, such as data poisoning and evasion attacks, employing techniques like influence-based training and adversarially adaptive training. Van has also explored the application of machine learning in specialized domains, including the analysis of noisy brain MRI data and the prediction of polymer properties using vision-language models. Additionally, his research extends to the ethical considerations of machine learning, with publications addressing fair machine learning and the utility loss associated with privacy techniques like local differential privacy. He has collaborated with several researchers at the University of Arkansas at Fayetteville, including Alycia N. Carey, Xintao Wu, M. S. Vinay, and Kennedy Edemacu, on multiple shared publications.
Metrics
- h-index: 5
- Publications: 30
- Citations: 139
Selected Publications
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Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges (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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Fair In-Context Learning via Latent Concept Variables (2025)
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Influence-based approaches for tumor classification in noisy brain MRI with deep learning and vision-language models (2025)
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Soft Prompting for Unlearning in Large Language Models (2025)
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Selecting In-Context Learning Demonstrations Via Influence Analysis (2025)
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Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis (2024)
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Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions (2024)
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On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study (2024)
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Robust Influence-Based Training Methods for Noisy Brain MRI (2024)
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HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks (2023)
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Defending Evasion Attacks via Adversarially Adaptive Training (2022)
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Poisoning Attacks on Fair Machine Learning (2022)
Collaboration Network
Top Collaborators
Xintao Wu
University of Arkansas at Fayetteville
11 shared publications
In database
- Poisoning Attacks on Fair Machine Learning
- In-Context Learning Demonstration Selection via Influence Analysis
- Robust Influence-Based Training Methods for Noisy Brain MRI
- Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
- Defending Evasion Attacks via Adversarially Adaptive Training
Showing 5 of 11 shared publications
Alycia N. Carey
University of Arkansas at Fayetteville
7 shared publications
In database
- Robust Influence-Based Training Methods for Noisy Brain MRI
- Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
- HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
- HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
- Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
Showing 5 of 7 shared publications
Xintao Wu
University of Arkansas at Fayetteville (US)
5 shared publications
- Poisoning Attacks on Fair Machine Learning
- Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
- 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
Aidong Lu
University of North Carolina at Charlotte (US)
3 shared publications
- Poisoning Attacks on Fair Machine Learning
- Defending Evasion Attacks via Adversarially Adaptive Training
- Poisoning Attacks on Fair Machine Learning
Kennedy Edemacu
University of Arkansas at Fayetteville
2 shared publications
In database
- Fair In-Context Learning via Latent Concept Variables
- Fair In-Context Learning via Latent Concept Variables
Chen Zhao
Baylor University (US)
2 shared publications
- Fair In-Context Learning via Latent Concept Variables
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
Prateek Verma
University of Arkansas at Fayetteville (US)
2 shared publications
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
- A Machine Learning Framework for Automated Computational Ethology Using Markerless Pose Estimation
Wei Du
University of Arkansas at Fayetteville (US)
1 shared publication
- Poisoning Attacks on Fair Machine Learning
Wei Du
1 shared publication
- Poisoning Attacks on Fair Machine Learning
Wei Du
University of Arkansas at Fayetteville (US)
1 shared publication
- Defending Evasion Attacks via Adversarially Adaptive Training
Feng Chen
The University of Texas at Dallas (US)
1 shared publication
- Defending Evasion Attacks via Adversarially Adaptive Training
M. S. Vinay
1 shared publication
In database
- In-Context Learning Demonstration Selection via Influence Analysis
Karuna Bhaila
1 shared publication
- Fair In-Context Learning via Latent Concept Variables
Chen Zhao
1 shared publication
- Fair In-Context Learning via Latent Concept Variables
Feng Chen
1 shared publication
- Fair In-Context Learning via Latent Concept Variables
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