Applying principles from systems engineering to real-world sociotechnical systems that support our civilization to make them more resilient against both global and targeted disruptions.
Our infrastructure and engineered systems that are the thread of our civilization combine traditional physical resources with cyber-technologies like sensor-actuator networks and decision-making algorithms.
As disruptions to these systems caused by natural disasters, security attacks or pandemics become more frequent and diverse, a proactive approach to protecting, monitoring and controlling these systems becomes all the more important.
Resilient systems safeguard society through the preservation of resources and maintaining clean energy, air, water and supply chains. Resilient systems are able to resist environmental stresses due to climate change, natural disasters or disease outbreaks that impact the health and well-being of society. The design of resilient systems requires an understanding of fundamental system properties, their interactions with human decision-makers, and the ability to model and predict the key uncertainties in a rapidly changing world. Our research in this domain is in the design and management of societal-scale infrastructure systems for the future.
Key areas include:
Engineering Sustainability: Analysis and Design
Introduces a systems approach to modeling, analysis, and design of sustainable systems. Covers principles of dynamical systems, network models, optimization, and control, with applications in ecosystems, infrastructure networks, and energy systems. Includes a significant programming component. Students implement and analyze numerical models of systems, and make design decisions to balance physical, environmental, and economic considerations based on real and simulated data.
Machine Learning for Sustainable Systems
Building on core material in 6.402, emphasizes the design and operation of sustainable systems. Illustrates how to leverage heterogeneous data from urban services, cities, and the environment, and apply machine learning methods to evaluate and/or improve sustainability solutions. Provides case studies from various domains, such as transportation and urban mobility, energy and water resources, environmental monitoring, infrastructure sensing and control, climate adaptation, and disaster resilience. Projects focus on using machine learning to identify new insights or decisions that can help engineer sustainability in societal-scale systems. Students taking graduate version complete additional assignments. Students cannot receive credit without simultaneous completion of the core subject 6.402
Network and combinatorial optimization methods and game-theoretic modeling for resilience of large-scale networks against disruptions, both random and adversarial. Topics include network resilience metrics, interdiction and security games, strategic resource allocation and network design, cascades in networks, routing games and network equilibrium models, reliability and security assessment of networked systems, and incentive problems in network security. Applications to transportation, logistics, supply chain, communication, and electric power systems.
Supply Chain and Demand Analytics
Focuses on effective supply chain and demand analytics for companies that operate globally, with emphasis on how to plan and integrate supply chain components into a coordinated system. Exposes students to concepts, models and machine learning, and optimization-based algorithms important in supply chain planning, with emphasis on supply chain segmentation, inventory optimization, supply and demand coordination, supply chain resiliency, and flexibility.