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 processing (communicating/compressing/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
Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021 / Fall 2022
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 / Spring 2020 / Spring 2021 / Spring 2022
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 5692 - Principles of Digital Communication
Introduces fundamental principles of efficient and reliable transmission of information used in wired and wireless digital communication systems including cable modems, smart phones/tablets, cellular networks, local area (wi-fi) networks, and deep-space communications. Topics include bandwidth and power constraints, digital modulation methods, optimum transmitter and receiver design principles, error rate analysis, channel coding potential in wired/wireless media, trellis coded modulation, and equalization.
ECEN 5712 - Machine Learning for Engineers
Prepares students to apply/improve machine learning methods for engineering applications and to perform related research. Covers popular algorithms and theories for learning from data, e.g., supervised learning, unsupervised learning, online learning, neural networks, VC-dimension, PAC learning theory. Explores the connections with detection/estimation theory and information theory. The course project focuses on engineering applications related to students� majors. Recommended prerequisites: ECEN 5612, 5652 and 5622.