Kenneth E. Barner

Prof. Kenneth E. Barner’s research centers on machine learning, artificial intelligence, and statistical signal processing, with applications spanning biomedical imaging, human–computer interaction, cybersecurity, and autonomous sensing systems. His work integrates deep learning, nonlinear and sparse signal processing, and graph signal processing to develop robust, data-efficient algorithms for challenging real-world environments. Recent projects focus on designing advanced machine learning systems for biomedical image segmentation in low-data scenarios, including deep denoising diffusion probabilistic models, domain generalization techniques, and graph-regularized adversarial training. His group also develops explainable AI methods for physiological signal analysis, such as ECG waveform detection and T-wave alternans identification. Additional research explores AI-driven multisensor data fusion for UAV-based 3D mapping, photogrammetry, and defect detection. Prof. Barner has made foundational contributions to nonlinear signal processing, earning IEEE Fellow recognition for his work. His theoretical innovations have been applied to diverse domains, including compressed sensing for wireless health monitoring, robust spectral imaging, and adaptive filtering for impulsive noise environments. He has published extensively in leading IEEE and Elsevier journals, advised numerous Ph.D. and M.S. graduates now in academia, industry, and government, and served as PI or co-PI on over $14M in research funding. His teaching portfolio includes courses on modern machine learning, engineering machine learning systems, statistical signal processing, Vertically Integrated Projects (VIP) utilizing drones and AI, and advanced topics in AI. Prof. Barner currently serves as Director of the Center for Cybersecurity, Assurance, and Privacy (CCAP) at the University of Delaware and holds a joint appointment with the Department of Biomedical Engineering.