Prof. Varanasi's recent research has been on long-standing as well as modern problems of establishing information-theoretic limits or fine approximations thereof of networks whose understanding would lead to a fundamental redesign of cellular networks and that model a broad range of practical scenarios spanning different topologies and settings including cellular, interference and multihop (mesh, wimax) networks with general messaging, multiple-antenna terminals, feedback, relaying and channel uncertainty models. Communication, signal processing algorithms and coding techniques for such networks have also been of interest.
Information Science and Engineering, theoretical foundations of handling (compressing/transmitting/storing) data over networks and extracting information from data, including information theory, theory of communication, wireless communications, coding and system optimization, signal processing and statistical/machine learning theory and their applications
ECEN 3810 - Introduction to Probability Theory
Covers the fundamentals of probability theory, and treats the random variables and random processes of greatest importance in electrical engineering. Provides a foundation for study of communication theory, control theory, reliability theory, and optics. Credit not granted for this course and MATH 4510 or APPM 3570.
ECEN 5622 - Information Theory and Coding
Spring 2018 / Spring 2019
Covers fundamental limits of data compression, reliable transmission of information and information storage.�� Topics include information measures, typicality, entropy rates of information sources, limits and algorithms for lossless data compression, mutual information, and limits of information transmission over noisy wired and wireless links. Optional topics include lossy data compression, limits of information transmission in multiple-access and broadcast networks, and limits and algorithms for information storage.
ECEN 5652 - Detection and Extraction of Signals from Noise
Introduces detection, estimation, and related algorithms. Topics in detection include simple/composite hypothesis testing, repeated observations and asymptotic performance and sequential detection. Topics in estimation include Bayesian estimation including minimum mean-square estimation and non-random parameter estimation. Topics in algorithms vary. Examples include algorithms for state estimation and smoothing in Hidden Gauss-Markov models and the expectation-maximization algorithm. Applications include communications, radar/sonar/geophysical signal processing, image analysis, authentication, etc.