Systems engineering is not about a specific technology, but how to harness them to improve the effectiveness of whole systems. This requires the ability to integrate information, analyze data, and use the resulting insights to improve decision making. The technical core is a set of mathematical tools rooted in optimization and probability theory.
Over time, these tools have become the fundamental building blocks of what is now called Artificial Intelligence (AI). At the same time, the scope of AI has changed from its original one of simulating or creating intelligence. Now, the focus is on augmenting intelligence (e.g. search engines and natural language translation) and, most relevant for systems engineering, the creation of pervasive intelligent infrastructure. This is a natural evolution of the tools and traditions of systems engineering to fit the AI-powered needs of the 21st century.
In recognition of this, the ESE department is sunsetting the Systems Science and Engineering undergraduate major. In its place is the new Artificial Intelligence, BSE Program, delivered in collaboration with the CIS department, that will prepare the next generation of students to leverage the power of AI and shape the future of engineering systems. We will continue to support all exiting SSE undergraduate students to their completion of the SSE degree by offering courses and advising. The Systems Engineering masters’ degree continues for those who want to pursue ‘traditional’ systems engineering at Penn.
“I initially chose to study Systems Engineering at Penn because I felt it would provide me with the skillset I needed to be successful while still being broad enough to allow me to choose from a variety of paths for my future. The most updated curriculum offers opportunities to pursue other interests, such as computer science and entrepreneurship. The combination of systems and computer science has helped me significantly in my pursuit of internships, and now a career. I cannot stress how much my experiences at Penn and the staff in the SSE department helped me to obtain both of these positions.”
“Penn is a leader in Systems Engineering not only for the curriculum itself, but in the ability to develop an interdisciplinary Systems Engineering framework by leveraging the broad amount of knowledge available across the University. As a student in the M&T Program for undergrad (studying Systems Engineering concurrently with business in Wharton), the interdisciplinary knowledge built upon a solid Systems Engineering framework only makes a Systems Engineering toolkit more meaningful, applicable, and — in the job market — more marketable. Even if you’re not in a dual degree program, Penn makes the application of Systems incredibly easy: not only can you take classes in any department of the engineering school, but you can take classes in Wharton, The College, Penn Law, the City Planning Department of PennDesign, and more!”
“Systems Engineering at Penn is one of the broader degrees in the Engineering school, which allowed me to handcraft my experience and specialize in a unique way which wouldn’t have been possible in other majors. Because of its grounding in statistics and mathematics, and because of its versatility, SSE afforded me the opportunity to major in both SSE and Mathematics, and gain exposure to mathematics in a variety of different settings. Mathematics has become the center of my undergraduate study, and I don’t think I could’ve learned so much about math in both pure and applied settings without pursuing both SSE and Mathematics.”
SSE faculty are leaders in systems engineering, optimization, and AI-driven decision-making. Their research advances intelligent infrastructure, data-driven modeling, and large-scale system integration, ensuring solutions to some of the world's most complex challenges.
Data, Systems, and Decisions: The purpose of this course is to introduce students to the basic concepts of systems engineering, data sciences, and machine learning. The course will cover the engineering cycle and expose students to the notions of data, systems, models, decisions, and requirements. The course empowers students to use statistical analysis, signal processing, and optimization techniques to process data in decision making systems. It also empowers students to use machine learning techniques for the same purpose. The relative strengths of each approach are discussed. Students are exposed to techniques to process data with temporal, spatial, and network structure as well as to deterministic and Markov dynamical system models.
Introduction to signal and information processing (SIP). In SIP we discern patterns in data and extract the patterns from noise. Foundations of deterministic SIP in the form of frequency domain analysis, sampling, and linear filtering. Random signals and the modifications of deterministic tools that are necessary to deal with them. Multidimensional SIP where the goal is to analyze signals that are indexed by more than one parameter. Includes a hands-on lab component that implements SIP as standalone applications on modern mobile platforms.
The course covers the methodological foundations of data science, emphasizing basic concepts in statistics and learning theory, but also modern methodologies. Learning of distributions and their parameters. Testing of multiple hypotheses. Linear and nonlinear regression and prediction. Classification. Uncertainty quantification. Model validation. Clustering. Dimensionality reduction. Probably approximately correct (PAC) learning. Such theoretical concepts are further complemented by exempla r applications, case studies (datasets), and programming exercises (in Python) drawn from electrical engineering, computer science, the life sciences, finance, and social networks.
