Kenneth Barner

Kenneth Barner is the Charles Black Evans Professor of Electrical Engineering at the University of Delaware, College of Engineering. His research focuses on signal and image processing, nonlinear systems, and human-computer interaction. He explores sparsity-based methods for biomedical signal analysis (e.g., EEG/ECG) and imaging applications like facial/gait recognition. His work addresses challenges in harsh environments and develops robust processing frameworks for medical and multimedia systems. His recent publications span deep learning applications in ECG analysis, malware detection, and medical imaging.

Barner's research integrates theoretical advancements with practical implementations, including compressive sensing, adversarial training, and graph-based regularization. His contributions bridge signal processing fundamentals with modern AI techniques, addressing critical needs in healthcare, cybersecurity, and multimedia analysis. His work on manifold learning for cardiac signals and entropy-based malware classification exemplifies his interdisciplinary approach.

Key themes include exploiting sparsity/nonlinearity in signal processing, developing explainable AI for medical diagnostics, and enhancing adversarial robustness in machine learning models. His lab's projects often involve collaboration across engineering, healthcare, and computer science domains.