My research advances information theory, Bayesian statistical signal processing, and artificial intelligence to discover causal interactions, quantify uncertainty, and build trustworthy inference systems for complex dynamical processes.
Research program
My work develops Bayesian and information-theoretic methodologies for nonlinear, non-Gaussian, and time-varying systems, with applications spanning speech intelligence, neuroscience, climate informatics, remote sensing, and industrial analytics.
Transfer entropy, mutual information, dependency analysis, causal discovery, and trustworthy inference.
Particle filtering, Kalman filtering, MCMC, probabilistic modeling, and uncertainty quantification.
Generative AI, interpretable machine learning, sequential inference, and complex dynamical systems.
Teaching
Digital Signal Processing, Statistical Signal Processing, and Generative Artificial Intelligence.
Signals and Systems, Feedback Control Systems, and Introduction to Robotics.
Circuit Theory and Telecommunications.
Professional service
Editorial Board Member, Entropy; MDPI Books editor; former Frontiers in Medicine Topic Editor.
MÜDEK Accreditation Coordinator, ERASMUS Department Coordinator, curriculum and departmental service.
IEEE Senior Member and reviewer in signal processing, information theory, AI, and biomedical engineering.