Teaching
ECE718 Convex Optimization (grad)
Fall 2019; Fall 2022; Spring 2024
This course focuses on theory, algorithms, and applications of convex optimization. Convex optimization deals with the non-linear optimization problems where the objective function and the constraints of the problem are both convex. These problems appear in a variety of applications in diverse fields of science and engineering. This course will cover how to recognize, model, and formulate the convex optimization problems. Topics include: review of least-squares, convex sets and functions, convexity with reference to inequalities, linear optimization, geometric programming, duality (Lagrange dual function), norm approximation, geometric problems, algorithm (descent, Newton, interior-point). Implementation of optimization algorithm will be carried out in CVX (MATLAB based software for convex optimization).
ECE432 Mobile Communication System
Spring 2021; Spring 2022; Spring 2023; Fall 2023 (Graduate)
The course addresses the fundamentals of wireless communications and provides an overview of existing and emerging wireless communications networks. It covers radio propagation and fading models, fundamentals of cellular communications, multiple access technologies, and various wireless. Simulation of wireless systems under different channel environments will be an integral part of this course.
EE35609 MATHEMATICS FOR MACHINE LEARNING
Fall 2020
This course presents the fundamentals of machine learning and big data analysis along with the related mathematics background at an introductory level. It deals with advanced linear algebra, vector calculus, probability theory, and optimization tools. With the mathematical foundation, we will cover some key machine learning techniques, such as Linear Regression, Principal Component Analysis (PCA), Support Vector Machine (SVM), and Deep Neural Network (DNN) etc.
EE15570 engineering Linear algebra
Spring 2020; Spring 2023
This course covers the following topics: solving systems of linear equations; matrices and linear transformations; image and kernel of a linear transformation; matrices and coordinates relative to different bases; determinants; eigenvalues and eigenvectors; least-squares approximation. Finally, we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors etc.
EE31699 Digital Signal Processing
Spring 2019; Spring 2020
This course provides an introduction to digital signal processing for both undergraduate students. In this course, a detailed examination of basic digital signal processing operations including sampling/reconstruction of continuous time signals, Fourier and Z-transforms will be given. The Fourier and Z-transforms will be used to analyze the stability of systems, and to find the system transfer function. The discrete Fourier transform (DFT) and fast Fourier transform (FFT) will be studied. Finally, we will examine time and frequency domain techniques for designing and applying infinite impulse response (IIR) and finite impulse response (FIR) digital filters.Â
EE31591 Circuit Theory I
Fall 2018, Spring 2021; Spring 2024
The goal of this course to develop an understanding of the elements of electric circuits and the fundamental laws, general techniques such as nodal and mesh analysis, Thevenin and Norton equivalent circuits used in analyzing electric circuits, and develop phasor techniques for AC steady-state analysis of circuits. Study on energy storage elements will help students to understand the transient and the steady-state response of RLC circuits. The course also aims to introduce elementary electronic circuits such as operational amplifiers and their circuit models.
EE31592 Circuit Theory II
Spring 2018; Spring 2019
This course mainly focus on alternating current circuit analysis; phasors, sinusoidal steady-state analysis; ac power, rms values, three-phase systems and frequency response concepts. Magnetically coupled circuits, Filter design and analysis and Two-Port Networks; Introduction to system analysis in frequency domain: Laplace Transforms, Fourier Transforms and Fourier Series.
EE26218 Communications Engineering
Fall 2018, Fall 2019, Fall 2020, Spring 2022
This course presents the fundamentals of analog and digital communication techniques at an introductory level. It deals with continuous wave modulation, pulse modulation, baseband data transmission, and performance analysis of analog and digital communication systems in noisy environments.
EB68680 Discrete-Time Signal Processing (grad)
Spring 2018
This course begins with a discussion of the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time. The course proceeds to cover digital network and nonrecursive (finite impulse response) digital filters. Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.