Electrical Engineering, MSE
The MSE Program in Electrical Engineering gives students the theoretical and technological foundation needed to deal with the new ideas and new applications that are the hallmarks of modern electrical engineering. A major advantage of our MSE program is that it allows students to focus their education according to their interests and goals, from nanotechnology and circuits, to embedded systems or robotics. The MSE Program in Electrical Engineering gives students the theoretical foundation and the interdisciplinary skills needed to deal with the new ideas and new applications that are the hallmarks of modern electroscience. A major advantage of our MSE Program allows you to tailor your education to your own interests and goals, from Electromagnetics and Photonics, sensors and MEMS to VLSI and Nanotechnology.
For more information: http://www.ese.upenn.edu/current-students/masters/index.php
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.
Electrical Engineering Degree Requirements
10 course units are required for MSE in Electrical Engineering.1
| Code | Title | Course Units |
|---|---|---|
| EE Core | ||
| Select 5 required course units in any of the three areas below: 2 | 5 | |
| Physical Devices and Nano Systems | ||
| Quantum Circuits and Systems | ||
| Electromagnetic and Optics | ||
| Prin of Quantum Tech | ||
| The Physics of Solid State Energy Devices | ||
| Quantum Engineering | ||
| Nanoscale Science and Engineering | ||
| Introduction to Micro- and Nano-electromechanical Technologies | ||
| Nanofabrication and Nanocharacterization | ||
| Circuits and Computer Engineering | ||
| Internet of Things Sensors and Systems | ||
| IoT Edge Computing | ||
| IoT Wireless, Security, & Scaling | ||
| Smart Devices | ||
| System-on-a-Chip Architecture | ||
| Hardware/Software Co-Design for Machine Learning | ||
| Digital Integrated Circuits and VLSI-Fundamentals | ||
| Analog Integrated Circuits | ||
| Chips-design | ||
| Chips-measurements | ||
| RFIC (Radio Frequency Integrated Circuit) Design | ||
| Power Electronics | ||
| Mixed Signal Circuit Design and Modeling | ||
| Information and Decision Systems | ||
| Linear Systems Theory | ||
| Simulation Modeling and Analysis | ||
| Feedback Control Design and Analysis | ||
| Introduction to Optimization Theory | ||
| Introduction to Networks and Protocols | ||
| Graph Neural Networks | ||
| Estimation and Detection Theory | ||
| Elements of Probability Theory | ||
| Digital Signal Processing | ||
| Machine Learning for Time-Series Data | ||
| Statistics for Data Science | ||
| Principles of Deep Learning | ||
| Learning in Robotics | ||
| ESE Electives | ||
| Select 2 ESE electives 3 | 2 | |
| SEAS Electives | ||
| Select 1 SEAS elective 4, 5 | 1 | |
| Open Electives | ||
| Select 2 open electives 6 | 2 | |
| Total Course Units | 10 | |
- 1
Students must complete ten (10) course units at the graduate level (5000+).
- A maximum of two (2) graduate-level course units may be transferred from another school to apply towards the degree. These cannot have been used to fulfill requirements of an undergraduate degree.
- Students must be registered with the 5000-level course number to be eligible as a graduate level course. Any cross-listed section at the 4000-level or below is ineligible towards the degree.
- 2
Students can select any combination from this list, and are not limited to a single area.
- 3
Select any graduate-level ESE course at the 5000 and 6000 level.
- 4
Select 1 graduate-level course within: ESE, CIS, CIT, IPD, MEAM, MSE, EAS, or ENM. A maximum of two (2) CIT course units are allowed towards the degree.
- 5
Only the following EAS courses are allowed:
- EAS 5070 Intellectual Property and Business Law for Engineers
- EAS 5100 Technical Communication for Non-Native English Speakers
- EAS 5120 Engineering Negotiation
- EAS 5450 Engineering Entrepreneurship I
- EAS 5460 Engineering Entrepreneurship II
- EAS 5950 Foundations of Leadership
- ESE 6800 Special Topics in Electrical and Systems Engineering can be taken several times and counted more than once towards the degree. Each ESE 6800 Special Topics in Electrical and Systems Engineering course taken must address different topics to be eligible.
- A maximum of 1 ESE 5990 course unit can be used toward the degree.
- If a thesis is completed, it will count for 2 course units of ESE 9990 Master's Thesis).
- 6
Select from graduate courses at Penn in SEAS, SAS, Medicine, Law, Wharton MBA, Social Policy, and Education. These must have technical/scientific content and relevance to the student’s program. Approval must be obtained from the ESE department prior to enrollment in the course.
Students may select one concentration.
Artificial Intelligence
| Code | Title | Course Units |
|---|---|---|
| Two required courses: | ||
| ESE 5420 | Statistics for Data Science | 1 |
| CIS 5190 | Applied Machine Learning | 1 |
| or CIS 5200 | Machine Learning | |
| Choose two electives: | 2 | |
| Introduction to Optimization Theory | ||
| Graph Neural Networks | ||
| Machine Learning for Time-Series Data | ||
| Principles of Deep Learning | ||
| Deep Generative Models | ||
| Artificial Intelligence | ||
| Natural Language Processing | ||
| Machine Perception | ||
| Computer Vision & Computational Photography | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Total Course Units | 4 | |
Computer Engineering
| Code | Title | Course Units |
|---|---|---|
| Two required courses: | ||
| ESE 5700 | Digital Integrated Circuits and VLSI-Fundamentals | 1 |
| CIS 5710 | Computer Organization and Design | 1 |
| Choose two electives: | 2 | |
| System-on-a-Chip Architecture | ||
| Hardware/Software Co-Design for Machine Learning | ||
| Chips-design and Chips-measurements | ||
| Semiconductor Memory Devices and Circuit Design | ||
| Embedded Software for Life-Critical Applications | ||
| Operating Systems Design and Implementation | ||
| Total Course Units | 4 | |
Embedded Systems
| Code | Title | Course Units |
|---|---|---|
| Two required courses: | ||
| ESE 5190 | Smart Devices | 1 |
| CIS 5710 | Computer Organization and Design | 1 |
| Choose two electives: | 2 | |
| Feedback Control Design and Analysis | ||
| IoT Edge Computing | ||
| IoT Wireless, Security, & Scaling | ||
| Applied Machine Learning | ||
or CIS 5200 | Machine Learning | |
| Embedded Software for Life-Critical Applications | ||
| Total Course Units | 4 | |
Mixed-Signal/RF Integrated Circuits
| Code | Title | Course Units |
|---|---|---|
| One required course: | ||
| ESE 5720 | Analog Integrated Circuits | 1 |
| Choose three electives: | 3 | |
| Digital Integrated Circuits and VLSI-Fundamentals | ||
| Chips-design and Chips-measurements | ||
| RFIC (Radio Frequency Integrated Circuit) Design | ||
| Power Electronics | ||
| Mixed Signal Circuit Design and Modeling | ||
| High Frequency Power Electronics | ||
| Integrated Communication Systems | ||
| Total Course Units | 4 | |
Nanotechnology and Semiconductors
| Code | Title | Course Units |
|---|---|---|
| One required course: | ||
| ESE 5250 | Nanoscale Science and Engineering | 1 |
| Choose three electives: | 3 | |
| Electromagnetic and Optics | ||
| Internet of Things Sensors and Systems | ||
| The Physics of Solid State Energy Devices | ||
| Introduction to Micro- and Nano-electromechanical Technologies | ||
| Nanofabrication and Nanocharacterization | ||
| Digital Integrated Circuits and VLSI-Fundamentals | ||
| Analog Integrated Circuits | ||
| Chips-design and Chips-measurements | ||
| Total Course Units | 4 | |
Photonics and Quantum
| Code | Title | Course Units |
|---|---|---|
| Two required courses: | ||
| ESE 5100 | Electromagnetic and Optics | 1 |
| ESE 5130 | Prin of Quantum Tech | 1 |
| Choose two electives: | 2 | |
| Quantum Circuits and Systems | ||
| Quantum Engineering | ||
| Nanofabrication and Nanocharacterization | ||
| Integrated Photonic Systems | ||
| Total Course Units | 4 | |
Robotics
| Code | Title | Course Units |
|---|---|---|
| One required course: | ||
| ESE 5050 | Feedback Control Design and Analysis | 1 |
| Choose three electives: | 3 | |
| Linear Systems Theory | ||
| RoboRacer Autonomous Racing Cars | ||
| Nanorobotics | ||
| Learning in Robotics | ||
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Applied Machine Learning | ||
or CIS 5200 | Machine Learning | |
| Machine Perception | ||
| Total Course Units | 4 | |
| First Year | ||
|---|---|---|
| Fall | Course Units | |
| 3 CU's of EE Core Courses | 3 | |
| Course Units | 3.00 | |
| Spring | ||
| 2 CU's EE Core Courses | 2 | |
| 1 ESE Elective Course | 1 | |
| Course Units | 3.00 | |
| Second Year | ||
| Fall | ||
| 1 ESE Elective Course | 1 | |
| 1 SEAS Elective Course | 1 | |
| 1 Open Elective Course | 1 | |
| Course Units | 3.00 | |
| Spring | ||
| 1 Open Elective Course | 1 | |
| Course Units | 1.00 | |
| Total Course Units | 10.00 | |