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EE PhD Model Program

Electrical Engineering is a broad field which draws from disparate areas of knowledge, and so instead of providing rigid course expectations the faculty suggest EE students and their advisors consider the following courses as part of their program, depending on which aspect of EE their research is most aligned with. Programs will also often contain courses from SEAS, statistics, mathematics, physics, computer science or chemistry. This description of the Electrical Engineering PhD course guidelines augments the school-wide PhD course requirements. Students should make themselves familiar with both.

The EE faculty strongly recommend all Ph.D. students also attend to professional skills such as writing and giving presentations. Classes include ES 297 Professional Writing for Scientists and Engineers and ES 301 SEAS Teaching Practicum. Other resources include the GSAS Center for Writing and Communicating Ideas, the Derek Bok Center for Teaching and Learning, and the SEAS Graduate Council's Professional Development series.

The course lists below form a starting point for a discussion with the faculty about areas of interest. Students should work in close consultation with their advisors to develop an appropriate program plan that is consistent with the PhD Program's overall course requirements. Courses provide the background knowledge that is often needed to successfully complete research and allow students to learn more broadly about a field or related fields in a structured fashion.

Computer Engineering - suggested courses include

ES 202 Estimation and Control of Dynamic Systems
ES 250 Information Theory
ES 255 Statistical Inference with Engineering Applications
CS 283 Computer Vision
CS 243 Advanced Computer Networks
CS 244 Networks Design Projects
CS 246 Advanced Computer Architecture
CS 247r Advanced Topics in Computer Architecture
CS 249r Advanced Topics in Edge Computing
CS 252r Advanced Topics in Programming Languages
CS 261 Research Topics in Operating Systems
CS 262 Introduction to Distributed Computing
CS 265 Big Data Systems

as well as other courses listed under Devices and Circuits below

Control and Robotics - suggested courses include

ES 202 Estimation and Control of Dynamic Systems
ES 252r Advanced Topics in Robotics Research
ES 259 Advanced Introduction to Robotics
CS 283 Computer Vision
possible Decision Theory or Estimation courses:
- ES 201 Decision Theory
- ES 255 Statistical Inference with Engineering Applications
ES 250 Information Theory

as well as other courses listed under Devices and Circuits

Devices and Circuits - suggested courses include

CS 248 Advanced Design of VLSI Circuits and Systems
Physics 223 Electronics for Scientists
possible Devices courses:
- AP 195 Introduction to Solid State Physics
- AP 218 Electrical, Optical, and Magnetic Properties of Materials
- AP 295a Introduction to Quantum Theory of Solids
- AP 295b Quantum Theory of Solids
possible courses providing essential EE knowledge outside electronics:
- ES 250 Information Theory
- MIT 6.341 Discrete-Time Signal Processing
- ES 202 Estimation and Control of Dynamic Systems
possible Electromagnetism and Photonics courses:
- ES 273 Optics and Photonics
- ES 274 Quantum Devices
- AP 216 Electromagnetic Interactions with Matter
- AP 217 Applications of Modern Optics
- Physics 285a Modern Atomic and Optical Physics I

Photonics - suggested courses include

ES 273 Optics and Photonics
ES 274 Quantum Devices
AP 216 Electromagnetic Interactions with Matter
AP 217 Applications of Modern Optics
possible Solid State Physics courses:
- AP195 Introduction to Solid State Physics
- AP 295a Introduction to Quantum Theory of Solids
possible Quantum Mechanics courses:
- Physics 143b Quantum Mechanics II
- Quantum Science and Engineering (QSE 200/ES200)
- Chemistry 242 Quantum Mechanics for Physical Chemistry
- Physics 251a Advanced Quantum Mechanics I
possible Electromagnetism courses:
- Physics 232 Advanced Classical Electromagnetism
- ES 151 Applied Electromagnetism
possible other courses:
- ES 250 Information Theory
- ES 173 Introduction to Electronic and Photonic Devices
- ES 277 Microfabrication Laboratory
- AP 218 Electrical, Optical and Magnetic Properties of Materials
- AP 225 Introduction to Soft Matter
- AP 284 Statistical Thermodynamics
- AP 291 Electron Microscopy Lab
- Physics 223 Electronics for Scientists
- Physics 262 Statistical Physics

Signals and Information Processing - suggested courses include

ES 201 Decision Theory
ES 202 Estimation and Control of Dynamic Systems
ES 250 Information Theory
ES 255 Statistical Inference with Engineering Applications
possible Digital Communications courses:
- CS 283 Computer Vision
- MIT 6.450 Principles of Digital Communications
possible Applied Probability and Applied Statistics courses:
- Statistics 210 Probability I
- Statistics 211 Statistical Inference I
- CS 223 Probabilistic Analysis and Algorithms
- Statistics 220 Bayesian Data Analysis
- MIT 6.262 Discrete Stochastic Processes
possible Math and Applied Mathematics courses:
- Math 112 Introductory Real Analysis
- Math 136 Differential Geometry
- AM 201 Physical Mathematics I
- ES 220 Fluid Dynamics
- MIT 6.255J Optimization Methods