Areas of Specialization

Undergraduate degrees in the Department of Electrical and Computer Engineering are organized around a core of required courses and a selection of elective courses from four Specialization Areas:

  • Computer Engineering: provides a broad background in computer systems engineering, including computer architecture, digital hardware engineering, software engineering, and computer systems performance analysis
  • Data Science & Systems: integrates the foundations, tools and techniques involving data acquisition, data analytics, data storage and computing infrastructure in order to enable meaningful extraction of actionable information from diverse and potentially massive data sources. Applications include wireless communication systems, digital signal processing, image processing, and networking
  • Neuroengineering: exploits engineering techniques to understand, repair, manipulate, or treat the diseases of human neural systems and networks 
  • Photonics, Electronics, and Nano-devices: encompasses studies of electronic materials, including nanomaterials, semiconductor and optoelectronic devices, lasers and their applications. 

Specialization Area Requirements

  • For BSEE, a minimum of six specialization area courses; For B.A., minimum of four.
  • For BSEE, three or more in one area; for BA, two or more in one area.
  • For both BSEE and BA, at least two areas.
  • For both BSEE and BA, each course must be at least 3 semester hours.
  • ELEC 301 (Signals, Systems, and Learning) can count as a specialization course for the BA degree.

The department may add or delete courses from the areas, and graduate courses and equivalent courses from other departments may be used to satisfy area requirements with permission. Graduate courses, in the 500-level series, can often count as specialization courses with Advisor's approval.

Unrestricted Electives

Additional courses to provide the BA minimum requirement of 120 semester hours or the BSEE minimum of 134 semester hours.

Specialization Electives

Computer Engineering* 
  • *The sequence of COMP 140, COMP 182, COMP 215 are recommended for the CE Area as these are pre-requisites for many of the CS courses
  • COMP 321 Introduction to Computer Systems
  • COMP 382 Reasoning About Algorithms
  • COMP 430 Introduction to Database Systems
  • ELEC 323 Principles of Parallel Programming
  • ELEC 411 Microwave Engineering
  • ELEC 421 Operating Systems and Concurrent Programming
  • ELEC 422 VLSI Systems Design
  • ELEC 423 Digital Integrated Circuits
  • ELEC 424 Mobile and Embedded System Design and Application
  • ELEC 425 Computer Systems Architecture
  • ELEC 426 Advanced Digital Integrated Circuits Design
  • ELEC 429 Introduction to Computer Networks
  • ELEC 434 Advanced High-Speed System Design
  • ELEC 437 Introduction to Communication Networks
  • ELEC 442 Introduction to Analog Integrated Circuits
Data Science & Systems
  • COMP 330 Tools and Models for Data Science
  • DSCI 302 Data Science Tools and Models
  • DSCI 303 Machine Learning for Data Science
  • ELEC 306 Applied Electromagnetics
  • ELEC 430 Digital Communication
  • ELEC 431 Digital Signal Processing
  • ELEC 432 Mobile Bio-Behavioral Sensing
  • ELEC 434 Advanced High-Speed System Design
  • ELEC 436 Fundamentals of Control Systems
  • ELEC 437 Introduction to Communication Networks
  • ELEC 439 Data and Systems
  • ELEC 447 Introduction to Computer Vision
  • ELEC 475 Learning from Sensor Data
  • ELEC 478 Intro to Machine Learning
  • ELEC 498 Introduction to Robotics
  • MECH 488 Design of Mechatronic Systems
  • STAT 413 Introduction to Statistical Machine Learning
  • ELEC 380 Introduction to Neuroengineering
  • ELEC 382 Introduction to Computational Neuroscience
  • ELEC 483 Machine Learning and Signal Processing for Neuroengineering
  • ELEC 484 Human Neuro Imaging
  • ELEC 485 Fundamentals of Medical Imaging I
  • ELEC 486 Fundamentals of Medical Imaging II
  • ELEC 488 Theoretical Neuroscience: From Cells to Learning Systems
  • ELEC 489 Neural Computation
Photonics, Electronics, and Nano-devices
  • ELEC 262 Introduction to Waves and Photonics
  • ELEC 306 Applied Electromagnetics (or PHYS 302)
  • ELEC 361 Quantum Mechanics for Engineers (or PHYS 311)
  • ELEC 365 Nanomaterials for Energy
  • ELEC 460 Physics of Sensor Materials and Nanosensor Technology
  • ELEC 461 Solid State Physics (or PHYS 412)
  • ELEC 462 Optoelectronic Devices
  • PHYS 416 Computational Physics