Teaching

McGill

BIEN462 - Engineering Principles in Physiological Systems (Fall 2015): Advanced undergraduate course. Review of Signals and Systems, Time and Frequency Domain Analysis, Differential Equation Representations, Parameter Estimation, Closed-loop Systems, Basic Control Principles, Electrophysiology, The Circulatory System, The Respiratory System, The Endocrine and Renal Systems

BMDE502 - Biomedical Modeling and Identification (Winter 2015, 2016 - with Prof. H. Galiana): Graduate course for M.S. and Ph.D. students: Random Signals and Linear Systems, Black-box vs. Grey-box System Representations, Linear/Nonlinear Models, Mapping from Continuous to Discrete-time Models, Identification Approaches for Linear and Simple Nonlinear Models.

University of Cyprus

ECE220 - Signals and Systems I (Spring 2009, 2010, 2011, 2012, 2013): Core curriculum course for 2nd year ECE students: Continuous Time Signals and Systems, Linear Time Invariant Systems, Convolution, Differential Equation Models, Frequency Response and Filtering, Fourier and Laplace Transforms.

ECE429 - Introduction to Digital Signal Processing (Spring 2011, 2012): Elective course for 4th year ECE students: Discrete-time signals and systems, Sampling and digital signal reconstruction, Decimation and interpolation, Z Transform, Discrete Fourier Transform (DFT), Algorithms for DFT computation - the Fast Fourier Transform (FFT), FIR and IIR digital filters, Random discrete-time signals, Power spectral density estimation, Applications and advanced methods of DSP.

ECE623 - Digital Signal Processing (Spring 2013, Fall 2013): Graduate course for M.S. and Ph.D. students: Discrete-time signals and systems, Random signals and linear systems, Sampling and reconstruction, Decimation and Interpolation, Discrete Time Fourier Transform and Fast Fourier Transform, Filter design, Power spectral density estimation, Autoregressive signal modeling, Hilbert transform, Spectrograms and Short time Fourier transform.

ECE636 - Systems Identification (Fall 2009, Fall 2011): Graduate course for M.S. and Ph.D. students: Random signals and linear systems, Models of linear and nonlinear systems, Nonparametric identification in the time and frequency domains, Model parametrizations, Parametric identification, Recursive identification, Identification of closed-loop systems,  Model order selection and validation, Input design, Identification of nonlinear systems.

ECE795 - Pattern Recognition (Fall 2010, Fall 2012): Graduate course for M.S. and Ph.D. students: Probability theory, Bayesian decision theory, Parameter estimation, Nonparametric density estimation, Linear classifiers, Neural Networks, Kernel methods, Support vector machines, Mixture models and expectation maximization, Principal and independent component analysis, Unsupervised learning.