Minh-Hao Van
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Biography and Research Information
OverviewAI-generated summary
Minh-Hao Van's research focuses on the application of artificial intelligence, particularly large language models and visual language models, to diverse scientific domains. Recent work includes empirical studies on large visual language models for medical imaging analysis and their role in microscope image analysis. Van also investigates machine learning for mitigating poisoning attacks on fair learning systems and explores soft prompting techniques for unlearning in large language models. Additionally, Van has contributed to a survey of AI for materials science, encompassing foundation models, LLM agents, datasets, and tools. Collaborations at the University of Arkansas at Fayetteville include extensive work with Xintao Wu, Alycia N. Carey, Prateek Verma, and Karuna Bhaila.
Metrics
- h-index: 6
- Publications: 30
- Citations: 144
Selected Publications
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A Framework for Automated Tracking of Morphologically Distinct Cell Populations in Time-Lapse Microscopy (2026)
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Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach (2026)arXiv (Cornell University) OpenAlex
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Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges (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 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)
Collaboration Network
Top Collaborators
- On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
- Poisoning Attacks on Fair Machine Learning
- Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
- Soft Prompting for Unlearning in Large Language Models
- A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
Showing 5 of 11 shared publications
- 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
- Influence-based approaches for tumor classification in noisy brain MRI with deep learning and vision-language models
- 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
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
- Fair In-Context Learning via Latent Concept Variables
- A Machine Learning Framework for Automated Computational Ethology Using Markerless Pose Estimation
- Poisoning Attacks on Fair Machine Learning
- Defending Evasion Attacks via Adversarially Adaptive Training
- Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
- Fair In-Context Learning via Latent Concept Variables
- An interdigital bandpass filter embedded in LTCC for 5-GHz wireless LAN applications
- An interdigital bandpass filter embedded in LTCC for 5-GHz wireless LAN applications
- An interdigital bandpass filter embedded in LTCC for 5-GHz wireless LAN applications
- Poisoning Attacks on Fair Machine Learning
- Defending Evasion Attacks via Adversarially Adaptive Training
- Defending Evasion Attacks via Adversarially Adaptive Training
- Selecting In-Context Learning Demonstrations Via Influence Analysis
- Soft Prompting for Unlearning in Large Language Models
- A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
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