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DTSTAMP:20260224T101438
DTSTART;TZID=America/Detroit:20260422T120000
DTEND;TZID=America/Detroit:20260422T160000
SUMMARY:Exhibition:Revolutionary Paine: Andy Murphy Student-Curated Class Exhibit Common Sense
DESCRIPTION:Thomas Paine’s “Common Sense” was one of the most influential works of the American Revolution. The first edition was published on January 10\, 1776\, with an initial print run of just 1\,000 copies\; but within weeks demand soared. The students of Andy Murphy’s POLISCI 495 course co-curated the exhibition “Revolutionary Paine” to document the whirlwind caused by its publication. On view at the Clements January 16-May 8\, weekdays from 12-4 pm.
UID:143999-21894488@events.umich.edu
URL:https://events.umich.edu/event/143999
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:history,Exhibition,Exhibit,Americana
LOCATION:William Clements Library - Avenir Foundation Reading Room
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20251218T085055
DTSTART;TZID=America/Detroit:20260422T120000
DTEND;TZID=America/Detroit:20260422T130000
SUMMARY:Lecture / Discussion:The Regulatory\, Property\, and Human Rights-Based Strategies for Protecting American Waterways
DESCRIPTION:Erin Ryan\, Associate Dean for Environmental Programs and Elizabeth C. & Clyde W. Atkinson Professor\, Florida State University College of Law\n\nThis analysis introduces a framework of three different strategies for protecting American waterways—the conventional regulatory approach\, an alternative property-based approach\, and a newer human rights-based approach—and reviews how the dynamic among them will be impacted by recent Supreme Court decisions impacting environmental law.  The rights of nature movement has emerged as a human rights-based approach to environmental protection\, the public trust doctrine offers a public property-based approach\, and the Clean Water Act epitomizes the more traditional regulatory approach. \n\nIn recent years\, however\, the Court issued a series of decisions that have unwound nearly a half-century of accepted regulatory practice\, limiting the reach of the Clean Water Act as a tool for protecting waterways in Sackett v. EPA\, weakening the reach of the Clean Air Act in West Virginia v. EPA\, and weakening environmental agencies more generally in Loper Bright Enterprises v. Raimondo. These cases will exact a cost for wise environmental governance under all three models reviewed here.
UID:142887-21891766@events.umich.edu
URL:https://events.umich.edu/event/142887
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biology,Sustainability,Social Justice,Social Impact,Science,Rackham,Public Policy,Public Health,Pre-Law,Politics,Law,Interdisciplinary,Graduate School,Graduate,Free,Faculty,Energy,Economics,Ecology,Discussion,Climate and Space Sciences and Engineering,Civil and Environmental Engineering,Activism,Outdoors
LOCATION:Jeffries Hall - 1020
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260410T093853
DTSTART;TZID=America/Detroit:20260422T120000
DTEND;TZID=America/Detroit:20260422T140000
SUMMARY:Workshop / Seminar:University Career Center Clothes Closet 10th Birthday Party
DESCRIPTION:University Career Center (UCC) Clothes Closet Turns 10 Years Old - Let’s Celebrate in Style! ✨\n\nThe UCC Clothes Closet is celebrating a decade of helping students build their brand\, and we want you there.\n\nWe’re officially double digits! Join us for the Clothes Closet 10th Birthday Party & Open House - a two-hour celebration full of fashion\, fun\, and free treats.\n\nStop by our 10th Birthday Party Open House for:\n• 🧥 A chance to  donate an Item/bring Blazers to fill a Rack\n• 🎡 Spin-the-wheel Games & Clothes Closet Trivia\n• 🧵 DIY + Art Activities\n• 🎂 Birthday Cake & Sweet Savory treats\n• 🎁 Fun swag to remember the day\n• 🏠 Renovation reveal + a look back at 10 years of impact\n• 📸 Fashion photoshoot (yes\, you’re the star)\n\nBring a friend. Leave with photos\, snacks\, and a little extra confidence.
UID:146250-21898718@events.umich.edu
URL:https://events.umich.edu/event/146250
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:University Career Center
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260422T121512
DTSTART;TZID=America/Detroit:20260422T120000
DTEND;TZID=America/Detroit:20260419T010000
SUMMARY:Sporting Event:Women's Lacrosse vs Penn State
DESCRIPTION:Women's Lacrosse vs Penn State
UID:147848-21902042@events.umich.edu
URL:https://events.umich.edu/event/147848
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Athletics,Athletics - Women's Lacrosse
LOCATION:U-M Lacrosse Stadium
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260407T121357
DTSTART;TZID=America/Detroit:20260422T120000
DTEND;TZID=America/Detroit:20260422T140000
SUMMARY:Presentation:Xiaofeng Dai - Dissertation Defense
DESCRIPTION:Please join Xiaofeng Dai for their dissertation defense titled \"What Does It Feel Like in the Nucleoid? The Biophysical Properties of the Bacterial Chromosome\".\n\n*Date:* Wednesday\, April 22nd\n*Time:* 12:00 PM\n*Where:* CHEM 1706\n\nZoom Meeting ID: 93579278960\nPassword: 199099
UID:147495-21901110@events.umich.edu
URL:https://events.umich.edu/event/147495
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Chemistry
LOCATION:Chemistry Dow Lab - 1706
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260402T141505
DTSTART;TZID=America/Detroit:20260422T123000
DTEND;TZID=America/Detroit:20260422T133000
SUMMARY:Workshop / Seminar:Robotics Undergraduate Group Declaration Meeting
DESCRIPTION:Join us for a group declaration ahead of the end of the Winter 26 term. Advisors will discuss declaration requirements and what it means to be a Robotics Undergraduate student.
UID:147361-21900897@events.umich.edu
URL:https://events.umich.edu/event/147361
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Engineering,Robotics,Undergraduate
LOCATION:Ford Robotics Building - 2000
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260422T122022
DTSTART;TZID=America/Detroit:20260422T123000
DTEND;TZID=America/Detroit:20260422T133000
SUMMARY:Workshop / Seminar:Robotics Undergraduate Group Declaration Meeting - Winter 2026
DESCRIPTION:Join us for a group declaration ahead of the end of the Winter 26 term. Advisors will discuss declaration requirements and what it means to be a Robotics Undergraduate student.\n
UID:147362-21900899@events.umich.edu
URL:https://events.umich.edu/event/147362
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:FRB 2000
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260415T154150
DTSTART;TZID=America/Detroit:20260422T130000
DTEND;TZID=America/Detroit:20260422T143000
SUMMARY:Workshop / Seminar:Marjorie Lee Browne Scholars Mini-Symposium
DESCRIPTION:You are invited to attend a mini-symposium featuring the 14th cohort of MLB scholars completing the program this month. Each MLB scholar will give a 25-minute presentation of their research and have 5 minutes to answer questions from the audience. The MLB mini-symposium will take place on Wednesday\, April 22 from 1:00-2:30pm in 1068EH. Food will be provided during the talks.\n\nPlease RSVP by Sunday\, April 19 so that we know how many people to expect and can order food accordingly. https://forms.gle/nY719sLt3CdtovG38\n---\nIan Augsburger: Efficient Learning of Dirichlet Simplex Models with Asymmetric Concentration Parameters\n\nAbstract: Learning latent topic or mixture models governed by Dirichlet distributions is a central problem in unsupervised learning\, with applications ranging from topic modeling to population genetics and biological mixture analysis. Existing approaches—most notably MCMC- and variational-based methods—are often computationally expensive\, sensitive to initialization\, and particularly brittle in regimes where the Dirichlet concentration parameters are asymmetric or highly skewed.\n \nIn this work\, we study the problem of efficiently learning Dirichlet Simplex Models\, with special emphasis on the practically important but underexplored setting of asymmetric concentration parameters and regimes where individual components dominate the mixture. We show that the second-order moment structure of the observed data encodes the simplex geometry up to an orthogonal transformation on a low-dimensional subspace. Exploiting this structure\, we reduce parameter recovery to the problem of learning an orthogonal map.\n \nBy introducing a geometry-aware metric aligned with the intrinsic covariance of the data\, we obtain a simplified optimization scheme over the orthogonal group that is both stable and fast. Our approach leverages the polytope geometry of the simplex to enable parallelization over symmetry classes\, significantly accelerating convergence. Empirically\, the resulting algorithm performs remarkably well for asymmetric Dirichlet models\, where standard MCMC-based methods often struggle. We view this framework as a step toward efficient\, geometry-driven learning algorithms for broader classes of latent variable models.\n---\nAmmar Eltigani: Random Matrix Theory for High-Dimensional Machine Learning\n \nAbstract: The modern high-dimensional regime—where the number of samples and their dimensions grow proportionally—is ubiquitous in machine learning applications such as finance\, healthcare\, wireless communications\, neuroscience\, and computer vision. Despite the remarkable success of large-scale models in this setting\, the underlying mathematical reasons remain only partially understood. Why\, for instance\, do overparameterized neural networks generalize well\, and why do their risk curves exhibit double descent?\n \nThis expository talk begins by examining how low-dimensional intuitions fail in high dimensions\, focusing on covariance estimation. We then introduce the Marčenko–Pastur law\, which describes the limiting spectral distribution of sample covariance matrices for white noise. Finally\, we discuss its generalization to arbitrary covariances and apply it to ridgeless linear regression\, deriving a theoretical risk curve that displays the double-descent phenomenon.\n---\nNicholas Simafranca: Learning Low-Dimensional Representations with Heteroscedastic Data Sources\n\nAbstract: Principal component analysis (PCA) is a fundamental method for dimensionality reduction\, but it treats all samples uniformly and can perform poorly when data come from sources with unequal noise levels. In this talk\, I begin with the classical and probabilistic viewpoints of PCA\, introducing probabilistic PCA (PPCA) as a latent-variable model for low-dimensional structure. I then discuss HePPCAT\, a heteroscedastic extension of PPCA that allows different groups of samples to have different noise variances. Unlike classical PCA\, the resulting maximum-likelihood problem is nonconvex and is not solved by a single eigendecomposition. I will describe how this problem can be approached through alternating majorization-minimization and explain how a Riemannian block MM framework gives a route to proving convergence of a proximalized HePPCAT algorithm to a stationary point.
UID:147779-21901962@events.umich.edu
URL:https://events.umich.edu/event/147779
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Graduate Students,Mathematics
LOCATION:East Hall - 1068
CONTACT:
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