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        "description":"Presenter Bio:\r\n\r\nVijay Subramanian is a Professor in the ECE Division of the EECS Department at the University of Michigan, Ann Arbor; from Fall 2014 to Summer 2024, he was an Associate Professor at the same institution. He received the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign,  Champaign, IL, USA, in 1999. He worked at Motorola Inc., the Hamilton Institute, Maynooth, Ireland, and the EECS Department, Northwestern University, Evanston, IL, USA; he also held an Adjunct Research Associate Professor in CSL and ECE at UIUC. His current research interests are in stochastic analysis, random graphs, multi-agent systems, and game theory (mechanism and information design) with applications to social, economic, and technological networks.\r\n\r\nAbstract:\r\n\r\nMulti-agent systems appear in many engineering and socioeconomic settings, wherein a group of agents or controllers interact with each other in a shared and possibly non-stationary environment, and make sequential decisions based on their own information using a (causal) interaction mechanism. \r\n\r\nIn this talk, we focus attention on cooperative sequential decision making under uncertainty\u2014a decentralized team, where a fixed finite number of agents act as a team with the common goal of minimizing a long-term cost function. We investigate the general situation where one long-term (objective) cost must be minimized, while maintaining multiple other long-term (constraint) costs within prescribed limits via a cooperative Multi-Agent Constrained Partially Observable Markov Decision Process (MAC-POMDP) model. Such constrained sequential team decision problems arise in several real-world applications where efficient operation must be balanced with maintaining safe operating margins\u2014such considerations arise in communication networks, traffic management, energy-grid optimization, e-commerce pricing, environmental monitoring, etc.\r\n\r\nWe focus on the discounted cost criterion, and start by establishing general results on Lagrangian duality and the existence of a global saddle-point. Next, we consider decentralized policy-profiles and their mixtures, and establish that when agents mix jointly over their policy-profiles, there is no (Lagrangian) duality gap, and a global saddle point exists under the Slater's condition. However, when agents mix independently over their policy-profiles, we show (through a concrete counterexample) that a non-zero duality gap can exist. Then, we consider coordination policies and their mixtures, and establish that, except for pure coordination policies, they are all equivalent to joint mixtures of decentralized policy-profiles. This equivalence result helps reformulate the original multi-agent constrained optimization problem into a single-agent constrained optimization problem, which is then used to propose a primal-dual framework for model-based optimal control. Finally, we extend the notion of a Multi-Agent Approximate Information State (MA-AIS) to constrained decision making, and formalize MA-AIS based coordination policies and their mixtures. We establish through a concrete counter-example that, (in contrast to behavioral coordination policies), MA-AIS based behavioral coordination policies and their mixtures are not equivalent. We also establish approximate optimality of mixtures of MA-AIS based coordination policies, and use this result to guide the development of a data-driven alternative for the aforementioned model-based primal-dual framework.\r\n\r\nThis is joint work with Nouman Khan, Amazon Search, Seattle, WA, which was carried out when he was a PhD student at the University of Michigan, Ann Arbor.",
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        "event_title":"UUWeekly: Fall Festival",
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        "event_title":"Biomedical Engineering Seminar Series",
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        "combined_title":"Biomedical Engineering Seminar Series: \"A Trio of [Very] Tiny Actuators,\" with Cameron Aubin, Ph.D.",
        "event_subtitle":"\"A Trio of [Very] Tiny Actuators,\" with Cameron Aubin, Ph.D.",
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        "description":"Abstract:\r\nThe nascent field of microrobotics is experiencing a \u201cCambrian explosion\u201d before our very eyes. Potential applications for these diminutive devices span an array of fields, including healthcare, exploration, environmental monitoring, search and rescue, industrial maintenance, and digital agriculture. However, the design of microrobotics systems is inherently tied to scaling-law constraints; as length scales decrease, surface forces and viscous forces (among others) begin to dominate inertial forces. This leads to fabrication bottlenecks, struggles with energy\/power autonomy, and the need for specialized and often unconventional actuators. \r\n\r\nIn this talk, I will present three unconventional microactuators developed in my own group. Each leverages distinct physical principles to achieve high forces, frequencies, power densities, and integration potential in microrobotic platforms. These innovations highlight both the limitations imposed by microscale regimes and the opportunities that emerge when we embrace nontraditional transduction mechanisms for locomotion and manipulation.",
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        "event_title":"IES Energy Seminar Series - From Retirement to Reuse: Unlocking the Full Potential of EV Batteries in Stationary Storage",
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        "combined_title":"IES Energy Seminar Series - From Retirement to Reuse: Unlocking the Full Potential of EV Batteries in Stationary Storage: Ziyou Song, University of Michigan, Electrical and Computer Engineering",
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        "description":"Abstract: \r\nMillions of electric vehicle (EV) batteries are projected to retire over the next decade, often retaining substantial capacity left. Repurposing these batteries offers a promising solution to address challenges in the battery industry, including raw material scarcity, supply chain constraints, and the need for a circular economy. However, current EV batteries mainly retire after their performance becomes unsatisfactory, leading to heterogeneous health conditions that diversify lifetime during second use. There is a clear need for guidance on reuse strategies to maximize their whole-lifecycle value, including reuse in lower-demand applications. Here, we assess over 101,000 active retirement-reuse-retirement scenarios for EV batteries across popular stationary energy storage applications. The whole-lifecycle value of EV batteries is quantified using real-world data from EV batteries and their digital twins. For EV batteries retired at different mileages, we simulated their remaining useful life under realistic second-use applications. The tradeoff between first-life benefits and second-lifetime benefits is analyzed, and recommendations for selecting second-use applications based on first-life usage conditions are investigated. Our results demonstrate the feasibility of repurposing retired EV batteries and underscore the importance of whole-lifecycle management to enhance circular economy.\r\n\r\nBiography:\r\nZiyou Song is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He earned his bachelor\u2019s degree with honors and Ph.D. degree with the highest honors in Automotive Engineering from Tsinghua University, China, in 2011 and 2016, respectively. Prior to joining the University of Michigan, Dr. Song served as an Assistant Professor at the National University of Singapore and worked as a battery algorithm engineer at Apple. Dr. Song\u2019s research focuses on modeling, estimation, optimization, and control of energy storage systems, especially for the electrified transportation and renewable energy sectors. Dr. Song has received several paper awards, including Automotive Innovation Best Paper Award, Applied Energy Highly Cited Paper Award, NSK Outstanding Paper Award of Mechanical Engineering, and IEEE VPPC Best Student Paper Award. Dr. Song serves as an Associate Editor and Editorial Member for IEEE Transactions on Transportation Electrification, IEEE Transactions on Power Electronics, Applied Energy, and eTransportation, and received the Outstanding Associate Editor award from IEEE Transactions on Transportation Electrification.",
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