• Contact Info
Publications in VIVO
 

Liu, Eugene

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • Dr. Liu's research is focused on the optimization of communication systems and networks, their performance analysis, and their performance upper limit characterization. The interests include artificial intelligence aided wireless and wireline communications, information theory and coding theory.

keywords

  • communication systems and networks, information theory, coding theory, machine learning

Publications

selected publications

Teaching

courses taught

  • ECEN 1100 - Exploring ECE
    Primary Instructor - Fall 2025
    Introduces students to areas of emphasis with the ECE department through seminars presented by faculty and outside speakers. Emphasizes career opportunities, professional ethics and practices, history of the profession, and resources for academic success. Several sessions promote team building and problem solving, and provide opportunities for first year students to meet their classmates.
  • ECEN 3300 - Linear Systems
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021
    Characterization of linear time-invariant systems in time and frequency domains. Continuous time systems are analyzed using differential equations and Laplace and Fourier transforms. Discrete time systems are analyzed using difference equations, Z-transforms and discrete time Fourier transforms. Sampling and reconstruction of signals using the sampling theorem. Applications of linear systems include communications, signal processing, and control systems. Degree credit not granted for this course and ECEN 3301.
  • ECEN 3810 - Introduction to Probability Theory
    Primary Instructor - Fall 2023 / Spring 2026
    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.
  • ECEN 5002 - Special Topics
    Primary Instructor - Spring 2020 / Spring 2021 / Fall 2024 / Fall 2025
    Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated for up to 9 total credit hours.
  • ECEN 5022 - Special Topics
    Primary Instructor - Spring 2023 / Spring 2025
    Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated up to 9 total credit hours.
  • ECEN 5612 - Random Processes for Engineers
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021 / Fall 2023 / Fall 2024 / Fall 2025
    Deals with random time-varying functions and is therefore useful in the broad range of applications where they occur. Topics include review of probability, convergence of random sequences, random vectors, minimum mean-square error estimation, basic concepts of random processes, Markov processes, Poisson processes, Gaussian processes, linear systems with random inputs, and Wiener filtering. Applications range from communications, communication networks, and signal processing to random vibration/stress analysis, mathematical finance, physics, etc.
  • ECEN 5692 - Principles of Digital Communication
    Primary Instructor - Spring 2018 / Spring 2019 / Summer 2020 / Spring 2021 / Summer 2021 / Spring 2022 / Spring 2024 / Spring 2026
    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
    Primary Instructor - Spring 2026
    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.
  • ECEN 6950 - Master's Thesis
    Primary Instructor - Fall 2021 / Spring 2022

Background

International Activities

geographic focus

Other Profiles