Electrical Engineering PhD

Degree Requirements

The following requirements are in addition to, or further elaborate upon, those requirements outlined in The Graduate School Policy Guide.

The PhD program in Electrical Engineering is divided into two Programs of Study:

  • Solid-State, Photonic, and Quantum Technologies
  • Signals and Systems

There are requirements common to both Programs as well as additional requirements specific to each Program.

Common Requirements

Course Requirements

  • EE requires a minimum of 15 graded courses that count for graduate (TGS) credit, not including ELEC_ENG 590-0.  The cumulative grade point average over these graded courses must be a B (3.0 GPA) or higher.  Courses that provide zero units of credit do not count toward these 15 units.
  • At least 6 of these 15 units must be 400- or 500-level courses.
  • At most 2 of these 15 units can be ELEC_ENG 499-0.  This course is reserved for projects that are not directly related to the research required for the PhD thesis or for readings in specific subjects for which the ECE Department has no regular courses. ELEC_ENG 499-0 is not intended to replace or augment the required units of ELEC_ENG 590-0.
  • All PhD students are required to complete the zero-credit Responsible Conduct for Research Training (GEN_ENG 519-0) during their first year.
  • A student's adviser may require more than the minimum number of courses. In such cases, the number of required ELEC_ENG 590-0 units will be reduced accordingly.

Other PhD Requirements

  • Teaching Requirement
  • Qualifying Examination
  • Prospectus
  • Dissertation
  • Final Exam (dissertation defense)

Additional requirements and processes are detailed in the Electrical Engineering Graduate Study Guide.

Solid-State, Photonic, and Quantum Technologies

Course Requirements

Total Units Required: 15

Course Title
Core courses in Solid-State, Photonic, and Quantum Technologies
Each student must take 5 of the following courses:
Photonic Information Processing
Fiber-Optic Communications
Nanotechnology
Fundamentals of Electronic Devices
Advanced Electronic Devices
Quantum Semiconductors
Quantum Electronics
Advanced Photonics
Nonlinear Optics
Fundamentals and Applications of Special Relativity
Area-specific courses in Solid-State, Photonic, and Quantum Technologies
Electives must be approved by the student's adviser, and may include the following courses:
Introduction to Communication Networks
Electronic Properties of Materials
Solid State Electronic Devices
Optoelectronics
Superconductivity and Its Applications
Quantum Optics
Computational Electrodynamics
Semiconductor Lasers
Random Processes in Communications and Control 1
Random Processes in Communications and Control 2
Distributed Optimization
Introduction to Nanoscale Lasers, Quantum Noise, Photons, and Measurement
Optical Communications
Information Theory and Learning
Selected Topics in Quantum Information Science and Technology
Advanced Communication Networks
Differential Equations of Mathematical Physics

Signals and Systems

Course Requirements

Total Units Required: 15

Course Title
Courses in Signals and Systems
Each student must complete a sequence of courses in an area of specialization according to the recommendation of the adviser. These courses may be in Signals and Systems and other areas. Courses in Signals and Systems may include:
Communications Systems
Introduction to Computer Vision
Introduction to Communication Networks
Digital Signal Processing
Introduction to Feedback Systems
Digital Filtering
Introduction to Digital Control
Digital Communications
Wireless Communications
Special Topics in Electrical Engineering (Cardiovascular Instrumentation)
System Theory
Advanced Digital Signal Processing
Digital Image Processing
Multimedia Signal Processing
Random Processes in Communications and Control 1
Random Processes in Communications and Control 2
Distributed Optimization
Signal Detection and Estimation
Optical Communications
Information Theory and Learning
Human Perception and Electronic Media
Advanced Computer Vision
Statistical Pattern Recognition
Deep Learning: Foundations, Applications, and Algorithms
Advanced Communication Networks
Adaptive Filters
Introduction to Nonlinear Control Theory
Deep Reinforcement Learning
Machine Learning: Foundations, Applications, and Algorithms
Advanced Digital Communications
Special Topics in Electrical Engineering (Cardiovascular Instrumentation)