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        "event_title":"Watcher of the Sky: Making and Remaking the Detroit Observatory",
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        "event_title":"Go Blue Career Jam: Resume Blitz",
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        "event_title":"NASA OSTEM Internships: How to Apply Virtual Workshop",
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        "event_title":"Towards End-to-End Safe Motion Planning for High-Dimensional Robot Systems",
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        "combined_title":"Towards End-to-End Safe Motion Planning for High-Dimensional Robot Systems: Robotics PhD, Challen Enninful Adu",
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        "description":"Committee Co-Chairs: Ram Vasudevan and Talia Moore\r\n\r\nAbstract: \r\nSafe motion planning is essential for deploying autonomous robots in real\u2011world environments, whether navigating cluttered warehouses, crowded public spaces, or unstructured outdoor settings. To perform reliably in these scenarios, robots must generate plans in real time, offer formal safety guarantees without undue conservatism, handle environmental uncertainty robustly, and tightly integrate visual perception with motion planning so they can react to changes as they occur.\r\n\r\nThere are several challenges associated with achieving safe autonomous navigation of mobile robots. First, generating dynamically feasible trajectories for high\u2011dimensional systems is computationally demanding, often requiring significant processing time to produce high\u2011quality solutions. Many safe motion planners treat these trajectories as high\u2011level plans (or waypoints) and rely on a lower\u2011level planner to enforce safety during execution. Consequently, overall performance hinges on the quality of the high\u2011level plan so suboptimal plans can force frequent replanning or emergency halts. Second, accounting for environmental uncertainty, especially in dynamic settings with uncertain agent motions remains difficult. Planners typically use worst\u2011case bounds on uncertainty, inflating safety margins and yielding overly conservative behaviors. Third, standard perception pipelines convert images into occupancy grids or decompose the scene into a set of convex primitives to simplify planning. These either discard fine geometric detail which may be useful for downstream task planning, or require significant and expensive preprocessing.\r\n\r\nThis dissertation proposes a cohesive suite of tools to address each challenge.First I present a collection of novel optimization\u2011based trajectory generators for high\u2011dimensional systems, built on the Affine Geometric Heat Flow (AGHF) PDE, which reformulates trajectory optimization as a curve\u2011shortening process solved by forward\u2011simulating an ODE system to its extremal curve. By combining pseudospectral collocation with spatial vector algebra, this framework dramatically reduces function evaluations and produces high\u2011quality, dynamically feasible trajectories in seconds for systems with dozens of degrees of freedom (e.g., 44\u2011dimensional legged robots) Second, I introduce a risk\u2011aware planning methodology that rigorously quantifies and bounds uncertainty via chance\u2011constrained optimization and reachability analysis. This method runs in real-time both in simulation and on hardware, and offers formal safety guarantees while avoiding the excessive conservatism of traditional uncertainty\u2011aware safe planners. Third, I propose a novel Gaussian splatting representation that allows probabilistic planning in radiance fields. By reformulating radiance field representations into a normalized Gaussian framework, and deriving tight upper bounds on collision probability this approach unifies perception and motion planning: the robot is able to use a single representation to reason about obstacle geometry while maintaining dense visual information that is useful for downstream task planning. Additionally, I present how this method can be applied on both fixed-base and mobile manipulators. Experiments in cluttered scenes and on a real\u2011world fixed base manipulator demonstrate significant improvements in planning speed and collision avoidance. Together, these contributions move us toward a unified architecture for safe, real\u2011time motion planning of high\u2011dimensional robotic systems in uncertain environments while closing the loop between perception and action with robust safety guarantees.\r\n\r\nIn person and on Zoom.\r\nZoom passcode: saferobo",
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        "combined_title":"Disrupting the Information Order in Health Care: Institutions, Policy Regimes, and the Value of Data: Denise Anthony, University of Michigan",
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        "description":"The 21st Century Cures Act API Rule seeks to increase the interoperability of patients\u2019 electronic health information (EHI) \u2013 individually identifiable patient information related to medical treatment, which can enable actors outside the institutional context and special data protections of health care to gain access to private data. We examine stakeholders\u2019 comments during the Notice of Public Rulemaking on the Cures Act API Rule to show not only how institutional context shapes their views, but also how the change to data flows may also disrupt established institutional meanings and mechanisms. The technical change of the API Rule leads insiders to defend, while enabling outsiders to disrupt institutions in health care. We show how disruption to existing institutional logics, relationships, and the information order challenges professional control of information, threatens provider-patient relationships, and upsets the property rights and data protections of private data in ways that may threaten the existing institutional order of health care in the United States.",
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        "description":"Abstract: In this talk, I will present work on collective behavior in dynamical systems. I will share numerical and mathematical results on self-synchronizing systems and provide a preview of how this understanding will guide the development of a self-driving laboratory. I will also pose open questions about how these ideas might be applied to digital biology. This work is supported by current and past DARPA grants.\r\n\r\nContact:  Peter Miller",
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