Data Driven Learning and Control seminar series is organized by the Information and Decision Science Lab at Cornell University and aims to explore the latest advancements and interdisciplinary approaches to data-driven learning and control systems.
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Compositional Design of Society-Critical Systems: From Autonomy to Future Mobility
When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, while insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles as part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
Bio: Gioele Zardini is the Rudge (1948) and Nancy Allen Career Development Assistant Professor at Massachusetts Institute of Technology. He is a principal investigator in the Laboratory for Information and Decision Systems, the Department of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems and Society. From January to June 2024, he was a postdoctoral scholar at Stanford University, working with Marco Pavone, sponsored by NASA. Zardini obtained his Ph.D. in Emilio Frazzoli’s group at the Institute for Dynamic Systems and Control, ETH Zurich. He received his B.Sc. and M.Sc. in mechanical engineering with focus in robotics, systems and control from ETH Zurich in 2017 and 2019, respectively.
Driven by societal challenges, the goal of his research is to develop efficient computational tools and algorithmic approaches to formulate and solve complex, interconnected system design and autonomous decision making problems. His interests include the co-design complex systems (all the way from future mobility systems to autonomous systems), compositionality in engineering, planning and control, and game theory.