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DTSTAMP:20240610T132829
DTSTART;TZID=America/Detroit:20240715T100000
DTEND;TZID=America/Detroit:20240715T160000
SUMMARY:Exhibition:Nicole Ray Art Exhibit: State of Play
DESCRIPTION:Dates: Saturday June 8 - Sunday August 25\n\nReception: Saturday June 8\, 2pm-4pm MBG West Lobby\n\nWhat is play? Who’s to say? The animals of these fields and woods\, streams and ponds surely know. They take time each day to adventure and roam\, scamper and scout. The plants and trees excitedly join in. Some bend and sway and some glisten in rain. Perhaps each invites their friends from away to come and show them new ways of play. Let’s have a look and spend the day imagining what happens when we look away. An exploration of encounters real and imagined by local artist\, Nicole Ray. \n\nBio\n\nNicole Ray is an artist and illustrator living in Brighton\, Michigan. She grew up in a small beach town in New York with her toes deep in the sand and her head buried in books. Nicole creates a whimsical line of art prints and paper goods under the name Sloe Gin Fizz.\n\nFrom quirky animal and vegetable characters to nostalgia-filled interiors and calming views of nature\, Nicole’s hand-drawn scenes are highly accessible\, infused with a playful sense of humor and a strong narrative quality. \n\nNicole holds a BFA in Illustration from the School of Visual Arts\, as well as a BA in History from Trinity College in Hartford\, CT. Nicole and her mister live in a log house on a lake just north of Ann Arbor with a spoiled border collie named Stella and an ever-expanding network of critter friends.
UID:122110-21848278@events.umich.edu
URL:https://events.umich.edu/event/122110
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Visual Arts,In Person,Free,Exhibition,Art
LOCATION:Matthaei Botanical Gardens
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240619T125323
DTSTART;TZID=America/Detroit:20240715T100000
DTEND;TZID=America/Detroit:20240715T130000
SUMMARY:Lecture / Discussion:Statistical Methods for Spatio-Temporal Tensor Data
DESCRIPTION:In recent years\, tensors have garnered significant attention from researchers across the domains of statistics\, applied mathematics\, and machine learning. The inherent multi-linear structure of tensors renders them an efficient means of representing high-dimensional data. The technological revolution in data collection and processing has led to the emergence of tensorial datasets across numerous scientific applications\, such as neuroimaging\, collaborative filtering\, and longitudinal data analysis. In this thesis\, we focus specifically on the analysis of tensors with spatial and temporal dimensionality\, commonly referred to as spatio-temporal tensors. We leverage the efficient tensor representation to analyze large-scale spatio-temporal data and integrate intricate spatio-temporal dependencies into the tensor model. Inspired by scientific applications in space weather monitoring\, we introduce novel statistical methodologies addressing four distinct challenges.\n\nI) The first part investigates the missing value imputation of spatio-temporal tensors with locally dependent missingness. Traditional low-rank matrix/tensor completion methods cannot provide reasonable imputations at locations where almost all data are missing in the neighborhood. We adopt the classic low-rank matrix completion framework and improve it by giving a tensor completion estimator exhibiting spatial and temporal continuity. We establish the convergence guarantee of the new method and apply it extensively to the global Total Electron Content (TEC) reconstruction problem.\n\nII) The second part dives into the uncertainty quantification (UQ) of tensor completion. Literature on the UQ of tensor completion relies heavily on the assumption that data is missing uniformly at random or at least independently and only applies to a restricted class of the completion method. We circumvent these restrictions by introducing a conformal prediction framework for the UQ. The resulting confidence intervals are constructed by properly accounting for the missing propensity of each tensor entry\, which is estimated by a low-rank tensor Ising model that can account for the dependent data missingness. We establish the theoretical coverage guarantee and validate the method through extensive simulations and an application to the global TEC reconstruction problem.\n\nIII) The third part focuses on the forecasting problem of matrix-valued spatial time series with auxiliary vector-valued\, non-spatial time series covariates. Existing works on matrix autoregression cannot handle such settings with predictors of non-uniform modes and spatio-temporal dimensions. We propose a novel semi-parametric matrix autoregression model incorporating the vector covariates with spatially smooth tensor coefficients. We establish the joint asymptotics of the autoregressive and tensor parameters under fixed and high-dimensional regimes and apply our method to the global TEC forecast problem.\n\nIV) The last part is dedicated to a scalar-on-tensor regression problem with multi-modal imaging tensor covariate. We encapsulate a tensor dimension reduction step and a Gaussian Process regression model in a single framework and introduce a total-variation regularization to capture spatially contiguous predictive signals. The new model complements the current literature by accounting for the interplay among different data modalities in an interpretable fashion. We apply our model to forecast the intensity of solar flares with multi-channel solar imaging data.
UID:122907-21849774@events.umich.edu
URL:https://events.umich.edu/event/122907
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240620T092551
DTSTART;TZID=America/Detroit:20240715T120000
DTEND;TZID=America/Detroit:20240715T130000
SUMMARY:Workshop / Seminar:Brain Connectivity in Cognitive Aging: A primer on concept\, measurement\, and application
DESCRIPTION:Join us for the Neuroimaging Summer Seminar Series\, showcasing the cutting-edge research of the neuroimaging faculty at the Michigan Alzheimer's Disease Research Center. This series aims to inspire both students and faculty to incorporate imaging techniques into their research\, while highlighting the pivotal contributions to neurodegeneration disease studies. Don't miss this opportunity to explore the forefront of neuroimaging research.\nRegistration is required. Please register online at\nhttps://michmed.org/Anj3n
UID:122862-21849724@events.umich.edu
URL:https://events.umich.edu/event/122862
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:medical research
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240606T142716
DTSTART;TZID=America/Detroit:20240715T120000
DTEND;TZID=America/Detroit:20240715T130000
SUMMARY:Workshop / Seminar:EEB Student Dissertation Defense - Bridget Shayka\, Phd Student
DESCRIPTION:Bridget Shayka presents their dissertation defense.\n\nEmail eeb.gradcoord@umich.edu for access to this seminar virtually.
UID:119850-21843670@events.umich.edu
URL:https://events.umich.edu/event/119850
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:biodiversity,Bsbsigns,AEM Featured,eeb,Ecosystems,ecosystem,Ecology And Evolutionary Biology,Ecology & Biology,ecology,Dissertation,Discussion,department of ecology and evolutionary biology,Biosciences,Biology
LOCATION:Biological Sciences Building - 1010
CONTACT:
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