EE Degree Concentrations

Concentrations are focused areas of study that provide students with a critical level of expertise in a particular domain within EE, preparing the student for employment or graduate school in that domain. To satisfy the requirements for a concentration, students must complete at least four courses from the selected concentration as detailed below.  

*Students may only pursue one Concentration. Please note: Students who are submatriculating MUST meet all of the concentration requirements BEFORE obtaining their undergraduate degree.

Form to declare a concentration

Mixed-Signal and RF Integrated Circuits

Circuits consist of interconnected elements that form the “brains” of complex electronic systems. They provide the physical platform to communicate, store and process information in electric form. These systems cover a wide range of real-life applications including wireless communications, Internet of Things, biomedical, sensing.

The Analog, Digital, and RF Integrated Circuits Concentration will equip students with the foundational knowledge to analyze and design analog and digital electronic systems consisting of transistors. Students will develop the ability to connect the physical and information world by developing complex modern electronic systems. Students will learn the operation of these systems through both analytical methods and simulations. View a one-page description here.

Must complete the following 3 courses:
ESE 319 Fundamentals of Solid-State Circuits
ESE 370 Circuit – Level Modeling, Design, and Optimization for Digital Systems
ESE 419 Analog Integrated Circuits
Select the 4th course from:
ESE 568 Mixed-Signal Design & Modeling
ESE 578 Radio Frequency Integrated Circuit Design
ESE 672 Integrated Communications Systems

System-on-A-Chip Design

SOC designs are powerful chips that run modern cell phones, tablets, electronic gadgets, and automobiles. To design these systems, engineers must understand all aspects of modern chip design.
The SOC concentration prepares students to analyze, design, and utilize modern chips that include multiple processors, memory, communications, and specialized accelerators. You will learn hardware/software co-design from transistors to digital logic to processors and accelerators to multi-processor systems to high performance and low power software. View a one-page description here.
Must complete the 4 courses:
ESE 350 Embedded Systems / Microcontroller
ESE 370 Circuit- Level Modeling, Design, and Optimization for Digital Systems
CIS 371 Computer Organization and Design
ESE 532 System-on-a-Chip Architecture

Photonics and Quantum Technology

Modern device technologies rely on the control of light and materials at the smallest scales. Examples include LED displays, lasers, optical transceivers, solar cells, photonic integrated circuits, sensors, medical therapies, secure communication systems, and quantum computers. This concentration will arm students with a broad understanding of optics and device nanofabrication, with options to further specialize in nanophotonics, integrated photonics, and quantum technology. Students completing these courses will be positioned to pursue advanced degrees or engage with industry to develop cutting-edge photonics technologies and quantum devices.
View a one-page description here.
Must complete:
ESE 330 Principles of Optics and Photonics
ESE 336 Nanofabrication of Electrical Devices
Select 2 courses from:
ESE 510 Electromagnetic and Optical Theory
ESE 513 Principles of Quantum Technology
ESE 523 Quantum Engineering
ESE 611 Nanophotonics: Light at the Nanoscale
ESE 673 Integrated Photonics Systems

Microsystems and Nanotechnology

The Microsystems and Nanotechnology concentration provides students with a fundamental knowledge of how transistor, semiconductor, photonic, and electromechanical devices operate and are fabricated. The elective courses allow students to specialize and gain a deeper understanding in specific areas such as electromagnetics and antennas, microelectromechanical systems (MEMS), deeply scaled CMOS, next-generation transistor technologies, quantum principles and devices, and nanoscience. View a one-page description here.
Must complete:
ESE 336 Nanofabrication of Electrical Devices
Select 3 courses from:
ESE 330 Principles of Optics and Photonics
ESE 510 Electromagnetic and Optical Theory
ESE 521 The Physics of Solid State Energy Devices
ESE 525 Nanoscale Science and Engineering
ESE 529 Micro-Electromechanical Systems (MEMS)
ESE 621 Nanoelectronics


The pursuit of robotics involves the theory, design, programming, and testing of intelligent machines that perform functions in our world’s broad variety of environments. A major barrier to the development of agile robots that can operate effectively in unstructured environments is the real-time coordination of their many motions, in response to the noisy data streams coming in from their sensors. To build and use robotic systems, engineers must understand the complex models and advanced mathematical methods required to control them. Students will also be equipped with an understanding of the functional capabilities and limits of the many different hardware and software tools available to guide the design and deployment of these robotic systems. View a one-page description here.
Must complete:
ESE 421 Control for Autonomous Robots
Select 3 courses from:
ESE 505 Control of Systems
ESE 512 Dynamical Systems
ESE 500 Linear Systems Theory
ESE 650 Learning in Robotics
MEAM 520 Introduction to Robotics
MEAM 620 Robotics

Data Science

Data Science (DS) is an interdisciplinary field aiming to extract knowledge and insights from complex datasets using tools from probabilistic modeling, statistical inference, machine learning, and engineering. The DS concentration will equip students with the foundational knowledge and technical expertise to understand the tools needed to transform unstructured sources of information into actionable decisions in engineering domains.  The students will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. Students in this concentration will develop the ability to responsibly collect and manage data, think critically, and make data-driven decisions. View a one-page description here.
Must complete:
ESE 305 Foundations of Data Science
ESE 402 Statistics for Data Science
Select 2 courses from:
CIS 520  Machine Learning
ESE 545 Data Mining: Learning from Massive Datasets
ESE 546 Principles of Deep Learning
CIS 545  Big Data Analytics
ESE 650 Learning in Robotics
NETS 312 Network Theory