The Societal Application Elective requirement is designed to give SSE majors the opportunity to explore the existing or potential use of systems engineering tools and concepts to address pressing societal challenges, needs and opportunities. The following list of approved courses is not intended to be exhaustive but rather illustrative of the broad diversity of offerings in other departments across the University that can be used to fulfill this requirement. All three courses must be chosen from the main list below. If a student wishes to make an exception, the request will need the undergraduate chairs final approval.

Biological Systems

• PHYS 280 Physical Models of Biological Systems
• BE 440 Biomolecular and Cellular Engineering
• BE 444 Nanoscale Systems Biology
• BE 445 Engineering and Biological Principles in Cancer
• BE 486 Signal Analysis and Processing (prereq BE301, but maybe ESE224 adequate)
• BE 566 / ESE 566 Networked Neuroscience
• BE 584 Mathematics of Medical Imaging and Measurements
• BIOL 437 Introduction to Computational Biology & Biological Modeling

Biology is undergoing a rapid revolution from a descriptive to a quantitative science. Because every aspect of social life is impacted in many different ways by insights and methods from biology, there are many opportunities to apply systems thinking and tools to the emerging new biological disciplines with significant social impact. The present list includes courses for which the quantitative aspects of the science are well enough established that the links to model based systems concepts and tools are immediate.

Human Factors

•  ESE 543 Human System Engineering

Climate

• EAS 301 Climate Policy and Technology
• CBE 375 Intro to Environmental Systems
• CBE 543 Sustainable Development and Water Residential Systems
• OIDD 261 Risk Analysis and Environmental Management
• ENVS 325 Sustainable Goods
• BEPP 261 Risk Analysis and Environmental Management
• BEPP 305 Risk Management

Energy

• EAS 401 Energy and Its Impacts: Technology, Environment, Economics, Sustainability
• EAS 402 Renewable Energy and Its Impacts: Technology, Environment, Economics, Sustainability
• EAS 403 Energy Systems and Policy
• ENMG 502 Intro to Energy Policy
• MEAM 503 Direct Energy Conversion: from Macro to Nano

There is by now a broad and deep agreement within the climatological sciences that anthropogenic disturbances have begun to change the earth’s climate in ways that will have increasingly dramatic social impact. The present list includes courses respecting which the links to quantitative systems concepts and tools are immediate.

Quantitative Modeling

• OIDD 220 Introduction to Operations Management
• OIDD 224 Service Operations Management
• OIDD 319 Advanced Decision Systems: Evolutionary Computation
• OIDD 353 Mathematical Modeling Applications in Finance
• FNCE 237 Data Science for Finance
• FNCE 392 Financial Engineering
• ECON 262 Market Design
• STAT 520 Applied Econometrics I

Business systems offer myriad engineering opportunities with huge social impact at the interface between “soft” behavioral, descriptive phenomena and “hard,” formally represented quantitative processes. The present list includes courses that focus on expanding and exploiting the role of formal representations and quantitative analysis in business systems applications.

City Planning

• CPLN 501 Quantitative Planning Analysis Methods
• CPLN 505 Planning by Numbers
• CPLN 520 Introduction to Community and Economic Development
• CPLN 550 Introduction to Transportation Planning
• CPLN 621 Metropolitan Food System

Transportation

• CPLN 550 Introduction to Transportation Planning
• CPLN 650 Transportation Planning Methods (pre-req CPLN505)
• CPLN 654 Urban Transit Systems and Technology
• CPLN 750 / ESE 550 Advance Transportation Seminar, Air Transportation Systems Planning
• ESE 550 Advance Transportation Seminar

Within the domain of social studies, the design and management of human transportation systems offers one of the most established venues for formal modeling and analysis. The present list includes courses respecting which the links to model based systems concepts and tools are immediate.

Chemical Processing

• CBE 520 Modeling, Simulations, and Optimization of Chemical Processes

Although many of the technical engineering electives available to SSE majors already address systems properties of physical devices and technology, our present SSE curriculum does not offer many connections to chemical engineering systems despite their huge social impact and importance. The present list is intended to facilitate making that connection.

Communications

• ESE 407 (ESE 507) Introduction to Networks and Protocols
• ESE 408 Data Communications

Today’s society benefits from a world-wide infrastructure that permits the exchange of information between physically separate points. Planning, optimization, and implementation of this infrastructure is rich in applications for systems engineering.

Robotics

• MEAM 410 / 510 Design of Mechatronic Systems
• MEAM 520 Introduction to Robotics
• MEAM 620 Advanced Robotics
• ESE 650 Learning in Robotics

Our society is increasingly exploiting human-built machines that manipulate objects in the physical world. The design, optimization, and deployment of these machines demands the trans-disciplinary skills embodied in systems engineering and leverages model-based system engineering.

Machine Intelligence

• CIS 519 Introduction to Machine Learning
• CIS 520 Machine Learning
• CIS 521 Fundamentals of AI
• CIS 581 Computer Vision and Computational Photography
• ESE 546 Principles of Deep Learning
• ESE 650 Learning in Robotics
• STAT 476 Applied Probability Models in Marketing

The use of computers to automatically identify patterns in data and give responses that would mimic or exceed the intelligent answer that a human would give is becoming a powerful tool to address many societal-scale problems, including business, health, manufacturing, transportation, and logistics. These courses build upon the quantitative signal and information processing and decision making skills in systems engineering and show how they can be expanded to engineer knowledge and learning systems.