Kuan Huang, Ph.D.
Assistant Professor
Office Location
GLAB 231
Phone
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Education
2016.09 - 2021.05, Ph.D. in Computer Science, Utah State University.
2012.09 - 2016.06, B.Eng. in Measurement Control Technique and Instruments, Harbin Institute of Technology.
Courses Taught
- CPS 4801: Artificial Intelligence
- CPS 4802: Machine Leaning Algorithms
- CPS 5801: Advanced Artificial Intelligence
- CPS 5802: Machine Learning Innovations
- CPS 3830: Machine Learning Foundations
- CPS 2231: Computer Programming
Research Interests
I'm interested in Artificial Intelligence, Computer Vision, Machine Learning, Deep Learning, Pattern Recognition, and Medical Image Analysis research.
Teaching Philosophy
Engage through Curiosity: I aim to spark students’ curiosity by connecting computer science concepts to real-world problems.
Learn by Doing: I emphasize hands-on practice through live coding, labs, and collaborative activities.
Integrate GenAI: I incorporate Generative AI tools to enhance creativity, problem-solving, and AI literacy in CS education.
Foster Inclusivity: I strive to build a supportive, engaging, and inclusive learning environment where every student can thrive.
Grants
External:
§ NSF CISE MSI Award #2430746 – Collaborative Research: CISE MSI: RCBP: SCH: Advancing Breast Cancer Detection in Ultrasound Imaging through Active and Weakly Supervised Learning Techniques, $203,981, Principal Investigator, 2024–2026.
§ CAHSI–Google Institutional Research Program – Weakly Supervised Image Segmentation with Image-Level Labels, $80,000 and $20,000 Google Cloud Platform credits, Principal Investigator, 2023–2024.
§ Oak Ridge Leadership Computing Facility (OLCF) – Large Language Models for Breast Cancer Detection, Frontier: 20,000 node hours; Andes: 2,500 node hours, Co-Principal Investigator, 2025.
§ OpenAI Researcher Access Program – Computational Access for AI Research, $10,000 OpenAI API credits, Principal Investigator, 2025.
Internal:
§ IST Research Fellowship – Breast Cancer Early Detection in Ultrasound Based on Weakly Supervised Techniques and Multi-Modal Models, $10,000, Principal Investigator, 2025–2026.
§ Students Partnering with Faculty (SpF) 2025 Awards – Breast Cancer Detection in Ultrasound Images Using Weakly Supervised Techniques and Multimodal Model, $11,500, Principal Investigator, 2025.
§ IST Research Fellowship – A Novel Trustworthy Weakly-Supervised Image Segmentation Framework, $10,000, Principal Investigator, 2023.
§ Students Partnering with Faculty (SpF) 2023 Awards – Deep Learning-Based Breast Ultrasound Image Analysis, $16,000, Principal Investigator, 2023.
Selected publications
o Kuan Huang, Meng Xu, and Yingfeng Wang. “Using Adversarial Training to Improve Uncertainty Quantification.” IEEE Transactions on Artificial Intelligence, 2025.
o Meng Xu, Yingfeng Wang, and Kuan Huang. “AnatoSegNet: Anatomy-Based CNN-Transformer Network for Enhanced Breast Ultrasound Image Segmentation.” IEEE International Symposium on Biomedical Imaging (ISBI), 2025.
o Kuan Huang, Yingtao Zhang, Heng-Da Cheng, and Ping Xing. “Reliable Multi-layer Segmentation of Breast Ultrasound (BUS) Images.” In Pattern Recognition and Computer Vision in the New AI Era, 2025.
o Kuan Huang, Yingtao Zhang, Heng-Da Cheng, and Ping Xing. “Shape-Adaptive Convolutional Operator for Breast Ultrasound Image Segmentation.” IEEE International Conference on Multimedia and Expo (ICME), 2021.
o Kuan Huang, Yingtao Zhang, Heng-Da Cheng, Ping Xing, and Boyu Zhang. “Semantic Segmentation of Breast Ultrasound Image with Fuzzy Deep Learning Network and Breast Anatomy Constraints.” Neurocomputing, 2021.