Apr 04, 2025  
2025-2026 General Catalog 
    
2025-2026 General Catalog

Electrical & Computer Engineering (Graduate Program)


 

For information regarding the UNDERGRADUATE PROGRAM, click here.   

Program Overview

The Division of Electrical & Computer Engineering in the School of Electrical Engineering & Computer Science offers programs of study leading to the MS and PhD degrees. Areas of study include Automatic Control (system identification, robust, adaptive, fault-tolerant, and networked feedback control); Communications and Signal Processing (digital, computer, and wireless communications, data compression, digital signal processing, and image processing); Computing (computer architectures, parallel and distributed computing, compilers, embedded systems, algorithms, and fault-tolerant computing); Electronics (electronic materials and devices, micro- and nano-technologies, nanophotonics, electro-optics, and biomedical sensors and systems); and Power (power electronics, harmonic analysis, electric machines, variable speed drive, power system stability and control, renewable energies, smart grid and energy conversion). An interdisciplinary concentration in Information Technology is also available.

There are approximately 50 students enrolled in Electrical and Computer Engineering MS and PhD programs. The division graduate faculty is comprised of nine professors, eight associate professors, and two assistant professors.

Administration

Shuangging Wei, Chair
Kidong Park, Graduate Advisor
TELEPHONE 225-578-5241
ECE ADMISSIONS INQUIRY E-MAIL eceapply@lsu.edu
WEBSITE www.lsu.edu/eng/ece/

Minor in Electrical Engineering

  • Graduate students from outside the division desiring a minor in electrical engineering must take at least nine credits of the division’s senior/graduate (4xxx) or graduate (7xxx) level courses. For a PhD student, six or more credits must be from 7xxx level courses; for an MS student, a minimum of three credit hours must be from graduate (EE 7xxx) level courses. The program must be approved by the Graduate Studies Committee.
  • The ECE Division does not require a separate examination for students minoring in electrical engineering.

Facilities

The division has datacenter-grade accelerators for work on machine learning, scientific computation, off-line animation, and more. In addition a Linux workstation laboratory equipped with high-end consumer-grade GPUs supports courses and student projects in graphics, machine learning, computer architecture, electronic design automation, and other topics.

The Electronic Material and Device Lab (with a Class-100 clean room) is utilized for research in semiconductor material growth, characterization, device fabrication, and measurements. The VLSI Systems Design Lab is used to design smart silicon chips and for device modeling. It is equipped with CAD tools and a high-speed data acquisition system for digital, analog/mixed-signal designs.

The McNeil RF/Communications Lab houses Copper Mountain and Keysight network analyzers, Keysight spectrum analyzers, signal generators, and Ettus software-defined radios. The PreSonus Digital Signal Processing Lab houses DSP development boards, code composer, and audio hardware. The Control Lab uses xPC Target for real-time seamless interfacing of Matlab/simulink and physical systems and hardware-in-the-loop designs.

The Power Electronics Lab offers hands-on experience with devices such as AC to DC converters, AC Voltage controllers, and circuit designs capable of handling large amounts of power. The Electric Machines Lab and Variable Speed Drive (VSD) Lab are equipped with conventional machines, as well as motors for special purposes. It also has power electronic inverters, and DSP boards for real-time simulation and control.

In addition, students can utilize the university resources of high-performance computing at the Center for Computation and Technology (CCT) and microfabrication and synchrotron beam-line capabilities at the Center for Advanced Microstructures and Devices (CAMD).

Admission

Applications for graduate study must be submitted through the online application site for the LSU Graduate School. Official transcripts must be sent directly from the registrar’s office or other appropriate official from your university to LSU Office of Graduate Admissions, 114 West David Boyd Hall, Baton Rouge, LA 70803. If your university offers electronic official transcripts, they can be sent to gradtranscripts@lsu.edu. Official test scores must be sent directly from the testing servicer. ETS test scores can be sent to LSU using the LSU Graduate School institution code of 6373; other test scores can be sent by selecting LSU from their list of universities. Photocopies of transcripts and test scores uploaded with your application may help speed up processing time but are not official. If you are admitted, the Graduate School will require official transcripts if you did not submit them with your application.

Meeting the minimum admission requirements established by the Graduate School does not necessarily ensure acceptance into the division’s graduate program. The division reviews the record of each applicant to assess promise for success at the graduate level, taking into consideration grade-point average, undergraduate preparation, recommendations, GRE scores (optional), TOEFL, IELTS, PTE, Duolingo English, or Michigan English Test (4-part skill test) scores (for international applicants), and any other pertinent information. Division recommendations are usually made within a month of the division receiving the complete application.

More details on applying to the division can be found at www.lsu.edu/eng/ece/academics/graduate/admissions/index.php.

Financial Assistance

The division attempts to provide financial support for all qualified doctoral students and for outstanding MS students.

Teaching Assistantships (TAs): All new applicants are eligible to apply for available teaching assistantships in ECE. Nearly all new teaching assistantships are usually awarded in the fall semester. Awards are competitive with usually more applicants than available TA positions. Only completed applications on file are reviewed when the award decisions are made. Hence, it is helpful to have your application file completed early. Applicants selected to receive assistantships will be notified by the division.

Research Assistantships (RAs): Research assistantships are selected individually by ECE faculty members. It is not uncommon for a prospective student to contact one or more faculty members whose research interest matches his/her own to determine if they have any open RA positions. Information on the research interests of faculty members is given on the division web page at www.lsu.edu/eng/ece/people/index.php.

The division also offers scholarships and fellowships funded through alumni and donors. More information on these awards can be found on our webpage www.lsu.edu/eng/ece/academics/graduate/index.php.

Graduate Faculty

(check current faculty listings by department here)

Pratul K. Ajmera (EM) • Semiconductor materials and devices, device physics, material growth and characterization, device fabrication, MEMS and integrated microsystems
Leszek S. Czarnecki (M) • Power electronics, nonsinusoidal systems, network analysis and synthesis
Theda Daniels-Race (M) • Nanoelectronic materials characterization and device fabrication, hybrid nanostructures and thin-film deposition
Mehdi Farasat (M) • Modeling and control of power electronic converters in renewable energy and electrified transportation systems, energy storage systems, wireless power transfer, intelligent control and energy management
Martin Feldman (EM) • Applied optics, x-ray lithography, micromachining
Guoxiang Gu (M) • Modeling and control of networked feedback systems, statistical signal processing with applications, and cooperative estimation and control
Amin Kargarian Marvasti (M) • Power systems operation and planning, resilience, machine learning, optimization, quantum computing
David M. Koppelman (M) • Computer architecture and microarchitecture, computational accelerators including GPUs and machine-learning accelerators
Xuebin Liang (M) • Coding theory and number theory, algorithm and complexity, wireless communications, information theory
Michael L McAnelly (3P) • Power system protection, operation and control
Shahab Mehraeen (M) • power systems stability and control, renewable energies, smart grid, energy conversion
Xiangyu Meng (6A) • Control theory and applications; perception, planning, control and coordination for autonomous agents, connected and autonomous vehicles, electric vehicles, intelligent transportation systems
Morteza Naraghi-Pour (M) • Wireless communication, wireless sensor and ad hoc networks, communication theory, telecommunication networks, neural networks, signal processing
Kidong Park (M) • Instrumentation for biotechnology and bioprocessing, bioreactor, cellular analysis, microfluidic devices 
Jagannathan Ramanujam (M) • Optimizing compilers, high-performance computing, embedded systems, low power computing, computer architecture
Jerry L. Trahan (M) • Algorithms, models of parallel computation, theory of computation, RFID protocols, robot algorithms
Ramachandran Vaidyanathan (M) •  Distributed computing, autonomous robot swarm coordination, algorithms, computing education, quantum algorithms
Georgios Veronis (M) • Theory and simulation of photonic materials and devices, nanoscale photonic devices, plasmonics, computational electromagnetics
Shuangqing Wei (M) • Information theory, communication theory, high-dimension statistics,  complexity theory, and their applications to fundamentals of machine learning, complex systems, and networks
Hsiao-Chun Wu (M) • Signal processing, machine learning, data science, artificial intelligence, wireless communications, human-machine interface, sensing technologies
Jian Xu (7M) • Biomedical instrumentation, bio-nano electronics, image-guided surgery, biomedical imaging
Xiangwei Zhou (M) • Wireless communications, signal processing and representation learning, federated learning in wireless networks, and the internet of things and artificial intelligence

Xugui Zhou (6A) • System dependability and security, cyber-physical systems, autonomous driving, healthcare, formal methods, trustworthy machine learning

Programs

    Master of Electrical EngineeringDoctor of Philosophy