Program Overview

Systems Engineering, MSE

The MSE Program in Systems Engineering (SE) provides students with a broad foundation across data science, systems modeling, and optimization and decision-making with applications in societal systems (energy, transportation, health operations). The program, grounded in the intersection of electrical and systems engineering, offers an in-depth theoretical foundation and interdisciplinary skills required by the growing complexity of technological systems. The flexible curriculum allows students to tailor their studies to their personal interests and goals, from signal processing, optimization, simulation, control, and cybernetics to complex adaptive systems, stochastic processes, and decision sciences.

Curriculum

The MSE in Systems Engineering requires 10 course units. The curriculum is structured as follows:

• Foundation Courses: 5 course units
  • Choose at least one course from each area of:
    • Data Science
    • Systems Modeling
    • System Design and Optimization
• Data Science courses include:
  • CIS 5190: Applied Machine Learning
  • CIS 5200: Machine Learning
  • ESE 5140: Graph Neural Networks
  • ESE 5280: Estimation and Detection Theory
  • ESE 5380: Machine Learning for Time-Series Data
  • ESE 5390: Hardware/Software Co-Design for Machine Learning
  • ESE 5420: Statistics for Data Science
  • ESE 5460: Principles of Deep Learning
  • ESE 6450: Deep Generative Models
  • ESE 6500: Learning in Robotics
• Systems Modeling courses include:
  • ESE 5000: Linear Systems Theory
  • ESE 5030: Simulation Modeling and Analysis
  • ESE 5070: Introduction to Networks and Protocols
  • ESE 5310: Digital Signal Processing
  • ESE 6650: Datacenter Architecture
  • ENM 5310: Data-driven Modeling and Probabilistic Scientific Computing
• System Design and Optimization courses include:
  • ESE 5060: Introduction to Optimization Theory
  • ESE 5050: Feedback Control Design and Analysis
  • ESE 5430: Human Systems Engineering
  • ESE 6050: Modern Convex Optimization
  • ESE 6190: Model Predictive Control
• ESE Elective: 1 course unit
  • Select 1 ESE Elective
• Technical Electives: 2 course units
  • Select 2 Technical Electives from any 5000 or 6000 level course in EAS, ENM, ESE, CIS, CIT, IPD, MEAM, or MSE
• Application Area: 2 course units
  • Choose ESE 9990 or any two graduate-level courses from one approved Application Area

Curriculum Requirements

• Students must complete ten (10) course units at the graduate level (5000+)
• Students must be registered in the 5000-level section in a cross-listed course. Any cross-listed section at the 4000-level or below is ineligible towards the degree.
• A maximum of two (2) CIT course units are allowed towards the degree.
• Only specific EAS courses are allowed, including:
  • EAS 5070: Intellectual Property and Business Law for Engineers
  • EAS 5100: Technical Communication and Academic Writing for Non-native Speakers of English
  • EAS 5120: Engineering Negotiation
  • EAS 5450: Engineering Entrepreneurship I
  • EAS 5460: Engineering Entrepreneurship II
  • EAS 5470: Engineering Product Management from Theory to Practice
  • EAS 5490: Engineering Entrepreneurship Lab
  • EAS 5950: Foundations of Leadership
• Application Area Electives:
  • Select 2 course units of approved electives from graduate courses offered 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.

Degree and Major Requirements

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.