Learning-Enabled Systems for Biomedical Engineering: Machine learning and deep learning for cardiovascular diagnostics, novel neural network architectures for physiological signal analysis, ECG arrhythmia detection and classification, clinical decision support systems. Signal processing for biomedical applications including wearable health monitoring, artifact suppression, and multimodal sensor fusion. Statistical foundations: Bayesian estimation, particle filtering, robust filtering theory, detection and estimation.
Signal Processing for Communications: Channel estimation, beam tracking, carrier frequency offset estimation, wireless network optimization, mobility management in 5G systems.
Keywords: ML/AI, Bayesian estimation, dynamic state system, nonlinear tracking, time-series estimation.