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TZID:America/Detroit
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DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20250908T080307
DTSTART;TZID=America/Detroit:20250605T120000
DTEND;TZID=America/Detroit:20250605T170000
SUMMARY:Exhibition:Watcher of the Sky: Making and Remaking the Detroit Observatory
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.\n\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.\n\nPresented by the Judy and Stanley Frankel Detroit Observatory\, part of the Bentley Historical Library.\n\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: \nThursdays\, 12-5 pm\nFridays\, 12-5 pm
UID:135958-21877543@events.umich.edu
URL:https://events.umich.edu/event/135958
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:educational,museums,Science,U-m History,university history,university of michigan history,Astronomers,astronomy,bentley historical library,bentley library,Education,Museum,Exhibition,free,history
LOCATION:Detroit Observatory
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250521T142953
DTSTART;TZID=America/Detroit:20250605T140000
DTEND;TZID=America/Detroit:20250605T160000
SUMMARY:Lecture / Discussion:Deep Learning-Assisted Approximate Bayesian Inference with Applications to Astronomy
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.
UID:135773-21877250@events.umich.edu
URL:https://events.umich.edu/event/135773
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250521T102816
DTSTART;TZID=America/Detroit:20250605T140000
DTEND;TZID=America/Detroit:20250605T160000
SUMMARY:Presentation:Rigorous Derivation of the Wave Kinetic Equation for \beta-FPUT System
DESCRIPTION:Abstract:\n\nWhile the WKE has been rigorously derived for the cubic nonlinear Schrödinger equation in dimensions d\ge 2 and for the Majda–McLaughlin–Tabak 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‑dimensional \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‑kinetic 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.
UID:135766-21877246@events.umich.edu
URL:https://events.umich.edu/event/135766
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics,Graduate Students,Graduate,Dissertation
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250522T132453
DTSTART;TZID=America/Detroit:20250605T150000
DTEND;TZID=America/Detroit:20250605T170000
SUMMARY:Other:Lawrence Sklar: A Celebration of Life
DESCRIPTION:3–5 pm Thursday 5 June 2025 \nThe Kuenzel Room. First Floor of the Michigan Union\n530 South State Street Ann Arbor\n\nZoom link details below. Password REQUIRED: 12345\nhttps://umich.zoom.us/j/9930741332?omn=95613966276\n\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.
UID:135570-21876955@events.umich.edu
URL:https://events.umich.edu/event/135570
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:philosophy
LOCATION:Michigan Union - Kuenzel Room
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250513T144304
DTSTART;TZID=America/Detroit:20250605T150000
DTEND;TZID=America/Detroit:20250605T160000
SUMMARY:Lecture / Discussion:Webinar: The Great Bay Eelgrass Resilience Project: Lessons Learned Doing Cutting Edge Science with Broad Community Input
DESCRIPTION:The Eelgrass Resilience Project was a three-year collaborative research effort designed to bridge science and management and address eelgrass habitat loss in the Great Bay Estuary\, NH. The estuary is currently classified as nitrogen impaired\, primarily due to significant declines in eelgrass (Zostera marina). Despite more than a decade of discussion\, uncertainty remains about the factors affecting eelgrass health and the role of nitrogen reduction—creating challenges for effective action.\n\nThis project brought together hydrodynamics\, biogeochemistry\, and ecology to explore how factors such as water residence time\, nitrogen loading\, in-situ nitrogen processing\, sediment dynamics\, and light availability influence eelgrass resilience. The team assessed spatial trends across the estuary and conducted a cutting-edge experiment to measure nitrogen processing along a flow path through an eelgrass meadow. In this webinar\, we’ll present our scientific methods\, key findings\, and project deliverables. We’ll also share insights from working with a Project Advisory Committee that connected our team with municipal and state decision-makers\, as well as national experts who provided real-time peer feedback throughout the project.
UID:135627-21877020@events.umich.edu
URL:https://events.umich.edu/event/135627
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sustainability,Environment
LOCATION:Off Campus Location
CONTACT:
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