Teaching

EE 556: Stochastic Systems & Reinforcement Learning


Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.


Last Taught: Spring 2021

EE 512: Stochastic Processes


Probability theory and stochastic processes, including renewal theory, Markov chains, Brownian motion, martingales, and stochastic calculus. Applications in communication networks, queuing theory and financial systems.


Last Taught: Spring 2017

EE 503: Probability for Electrical & Computer Engineers


Rigorous coverage of probability, discrete and continuous random variables, functions of multiple random variables, covariance, correlation, random sequences, Markov chains, estimation, and introduction to statistics.


Last Taught: Spring 2020

EE 364: Introduction to Probability and Statistics for Electrical Engineering and Computer Science


Introduction to concepts of randomness and uncertainty: probability, random variables, statistics. Applications to digital communications, signal processing, automatic control, computer engineering and computer science.


Last Taught: Spring 2019

EE 598: Electrical Engineering Research Seminar


Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students.

Registration Restriction: Open only to Master’s and Doctoral Students.


Last Taught: Spring 2019

EE 648: Network Economics & Games


Economics of networks; game theory, mechanism design and auctions in networks; spectrum sharing mechanisms in communications; pricing of differentiated services; network security.


Last Taught: Fall 2010