Ky Luu Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
faculty
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Biography and Research Information
OverviewAI-generated summary
Ky Luu's research interests focus on mental health within academic and occupational contexts, with a stated aim to develop evidence-based recommendations for supporting students and employees, improving early screening systems, and informing mental health policies and preventive practices. His work has explored topics such as career decision-making anxiety among high school students and the interplay between job stress, work performance, and attitudes toward professional psychological help among employees. Luu has also investigated the roles of religiosity, stress, and filial piety in attitudes towards premarital sex and abortion among various populations, including university students.
In addition to his work on mental health and societal attitudes, Luu has been involved in projects utilizing advanced computational techniques. These include research on "Type-to-Track: Retrieve Any Object via Prompt-based Tracking," a hybrid quantum optimization framework for drone delivery, and multimodal hypergraph retrieval-augmented generation for understanding the public health crisis related to nicotine. Luu has served as Principal Investigator on three NSF grants totaling over $2 million, focusing on human-in-the-loop AI for arthropod identification, large-scale multi-modality learning for tobacco addiction identification via social media, and addressing hallucinations in trustworthy large-scale multi-modality learning systems. His scholarship metrics include an h-index of 3 across 13 publications with 17 citations.
Metrics
- h-index: 27
- Publications: 243
- Citations: 3,416
Selected Publications
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An advanced hybrid quantum tabu search approach to vehicle routing problems (2026)
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COBRA: A Continual Learning Approach to Vision-Brain Understanding (2026)
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DEGA: Dynamic Entropy Guided Adaptation (2025)
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QUADRO: A Hybrid Quantum Optimization Framework for Drone Delivery (2025)
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QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks (2025)
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Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding (2025)
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Diffusion-inspired quantum noise mitigation in parameterized quantum circuits (2025)
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Hybrid Quantum Tabu Search for Solving the Vehicle Routing Problem (2024)
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CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars (2024)
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Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding (2024)
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Quantum visual feature encoding revisited (2024)
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React: recognize every action everywhere all at once (2024)
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Video-Based Autism Detection with Deep Learning (2024)
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Multi-camera multi-object tracking on the move via single-stage global association approach (2024)
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CapsNet for medical image segmentation (2024)
Federal Grants 5 $2,496,229 total
CAREER: Addressing Hallucinations for Trustworthy Large-scale Multi-Modality Learning
Collaborative Research: EAGER: Human Brain Modeling via Quantum Machine Intelligence
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