Courses taught

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Rutgers University

• 16:332:541 Stochastic Signals and Systems (Fall 2022, Fall 2023)
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  Axioms of probability; conditional probability and independence; random variables and functions thereof; mathematical expectation; characteristic functions; conditional expectation; Gaussian random vectors; mean square estimation; convergence of a sequence of random variables; laws of large numbers and central limit theorem; stochastic processes; stationarity, autocorrelation and power spectral density; linear systems with stochastic inputs; linear estimation; independent increment; Markov, Wiener, and Poisson processes.

• 16:332:512 Nonlinear and Adaptive Control Theory (Fall 2024)
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  This course explores tools for modeling, analyzing, and controlling nonlinear systems, including fundamental solution properties, Lyapunov stability theory, passivity, input-output stability, feedback linearization, backstepping, control Lyapunov functions, and other foundational methods in nonlinear and adaptive control.

• 14:332:345 Linear Systems and Signals (Spring 2023)
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  Introduction to continuous- and discrete-time systems and signals, linear time-invariant (LTI) systems, analysis of signals and systems using Laplace and Z-transforms, frequency-domain representations using Fourier transforms, and applications in communications, control, and signal processing.

• 14:332:226 Probability and Random Processes (Spring 2024)
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  Probability and its axioms, conditional probability, independence, counting, random variables and distributions, functions of random variables, expectations, order statistics, central limit theorem, confidence intervals, hypothesis testing, estimation of random variables. Random processes and their characterization, autocorrelation function.