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        "event_title":"Watcher of the Sky: Making and Remaking the Detroit Observatory",
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        "combined_title":"Watcher of the Sky: Making and Remaking the Detroit Observatory",
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        "description":"The Detroit Observatory was once a hub of astronomical discovery that put the University of Michigan on the map as a world-class research institution. A century later, it was an abandoned building with an uncertain future. From cornerstone to keystone, from the first director to the people who saved it from destruction, explore the life of a historic observatory 170 years in the making.\r\n\r\n\"Watcher of the Sky\" is being developed by student docents at the Detroit Observatory. They are currently collaborating with a museum design firm on the final version of the exhibit, which will debut in fall 2025. We invite you to check out what they've done so far.\r\n\r\nPresented by the Judy and Stanley Frankel Detroit Observatory, part of the Bentley Historical Library.\r\n\r\n\"Watcher of the Sky\" is now on display at the Detroit Observatory (1398 Ann Street, Ann Arbor, 48109). View the exhibit during the Observatory's open hours: \r\nThursdays, 12-5 pm\r\nFridays, 12-5 pm",
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        "tags":["educational","Science","U-m History","university history","university of michigan history","Astronomers","astronomy","bentley historical library","bentley library","Education","museums","Exhibition","free","history","Museum"],
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        "event_title":"Deep Learning-Assisted Approximate Bayesian Inference with Applications to Astronomy",
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        "combined_title":"Deep Learning-Assisted Approximate Bayesian Inference with Applications to Astronomy: Declan McNamara",
        "event_subtitle":"Declan McNamara",
        "event_type":"Lecture \/ Discussion",
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        "description":"Approximate Bayesian methods provide a principled means for inference in settings in which exact posterior inference is intractable. In this work, I present methods for variational inference, an approach to approximate Bayesian inference in which an approximation to the posterior is selected by numerical optimization. The approaches and analysis primarily consider amortized variational inference, a class of techniques that leverages deep learning to obtain a mapping from data instances to variational approximations of the posterior. First, I present SMC-Wake, a likelihood-based approach for minimization of the forward KL divergence. This algorithm uses Sequential Monte Carlo (SMC) samplers to construct inexpensive particle approximations for training an inference network. Next, I present a study of neural posterior estimation (NPE) and its objective function, the expected forward KL divergence. This likelihood-free approach to amortized inference averages over large amounts of simulated data from the model to learn mappings from data instances to variational approximations of the posterior. I present an analysis of this approach from the perspective of neural tangent kernel (NTK) theory. Under certain conditions on the variational family and neural network mapping, I show that NPE optimizes a convex functional and reliably converges to a unique solution in the asymptotic infinite-width limit, despite the highly nonconvex nature of neural network optimization landscapes. Finally, I extend these results to posit a novel class of expressive variational families based on linear combinations of basis functions, and propose a procedure to adaptively fit these basis functions to parameterize complex distributions. When targeting the forward KL divergence within this framework, the objective is convex in the variational parameters, but nevertheless allows for practitioners to fit highly multimodal variational approximations to the posterior. We conclude with applications of these methods to difficult problems in astronomy, such as redshift estimation from astronomical images, and the task of detecting blended astronomical spectra.",
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        "datetime_start":"20250605T140000",
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        "event_title":"Rigorous Derivation of the Wave Kinetic Equation for \\beta-FPUT System",
        "occurrence_title":"",
        "combined_title":"Rigorous Derivation of the Wave Kinetic Equation for \\beta-FPUT System: Boyang Wu",
        "event_subtitle":"Boyang Wu",
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        "description":"Abstract:\r\n\r\nWhile the WKE has been rigorously derived for the cubic nonlinear Schr\u00f6dinger equation in dimensions d\\ge 2 and for the Majda\u2013McLaughlin\u2013Tabak model in d=1, there is still lack of rigorous justification for the \\beta-FPUT model whose sinusoidal dispersion and unconserved frequency shift pose additional obstacles. In this thesis, we establish the WKE for a reduced evolution equation, removing the nonresonant terms, from the one\u2011dimensional \\beta-FPUT chain. We work in the kinetic limit N \\to \\infty and \\beta \\to 0 under the scaling laws \\beta=N^{-\\gamma} with 0<\\gamma<1. The result holds up to the sub\u2011kinetic time scale T=N^{-\\epsilon}\\min(N, N^{5\/4\\gamma})=N^{-\\epsilon}T_{kin}^{5\/8} for \\epsilon\\ll1, where T_{kin} represents the kinetic (thermalization) timescale. We also prove a sufficient upper bound for the nonlinearity parameter $\\beta$ that allows one to perform the canonical transformation on the original evolution equation. This upper bound suggests a scaling between \\beta and N, which governs the importance of the non-resonant terms in the original equation. By applying the symplectic integrator method, we further develop numerical studies on the  \\beta-FPUT model, comparing the magnitudes of resonant and nonresonant sums across various nonlinearity strengths and particle numbers to verify the predicted \\beta-threshold.",
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        "tags":["Mathematics","Graduate Students","Graduate","Dissertation"],
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        "event_title":"Lawrence Sklar: A Celebration of Life",
        "occurrence_title":"",
        "combined_title":"Lawrence Sklar: A Celebration of Life",
        "event_subtitle":"",
        "event_type":"Other",
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        "description":"3\u20135 pm Thursday 5 June 2025 \r\nThe Kuenzel Room. First Floor of the Michigan Union\r\n530 South State Street Ann Arbor\r\n\r\nZoom link details below. Password REQUIRED: 12345\r\nhttps:\/\/umich.zoom.us\/j\/9930741332?omn=95613966276\r\n\r\nParking: There is a University lot with visitor spaces on Thompson between Jefferson and Wiliam and a large parking structure with entrances on Maynard and Thompson between William and Liberty.",
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        "tags":["philosophy"],
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