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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:Undergraduate,Robotics,Engineering
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:
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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:Mathematics,Graduate Students
LOCATION:East Hall - 1068
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260409T075103
DTSTART;TZID=America/Detroit:20260422T133000
DTEND;TZID=America/Detroit:20260422T153000
SUMMARY:Social / Informal Gathering:Newnan Study Break
DESCRIPTION:Take a break before finals and join us in the Newnan Advising Center for snacks\, swag\, crafts\, and games!
UID:147553-21901256@events.umich.edu
URL:https://events.umich.edu/event/147553
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Transfer Students,Newnan Lsa Academic Advising Center,Newnan Academic Advising,Newnan,Mindfulness,Advising,All Majors Welcome
LOCATION:Angell Hall - Newnan Academic Advising Center
CONTACT:
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DTSTAMP:20260422T132022
DTSTART;TZID=America/Detroit:20260422T140000
DTEND;TZID=America/Detroit:20260422T150000
SUMMARY:Workshop / Seminar:60 Minutes Around the Globe
DESCRIPTION:
UID:143518-21901003@events.umich.edu
URL:https://events.umich.edu/event/143518
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:International House Ann Arbor (921 Church Street)
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260415T103842
DTSTART;TZID=America/Detroit:20260422T140000
DTEND;TZID=America/Detroit:20260422T150000
SUMMARY:Workshop / Seminar:EEB Prelim Seminar Series - Selection on Cognition in the Wild
DESCRIPTION:Seminar Summary: Cognition refers to the processes by which animals acquire\, store\, and use information from the environment. These processes drive many adaptive behaviors\, but we know little about how cognitive traits evolve in wild populations. I will use Polistes wasps as a model to investigate the links between cognition\, behavior\, and fitness over multiple generations in the field. My dissertation will provide important information about the costs and benefits of individual variation in cognition.
UID:147754-21901935@events.umich.edu
URL:https://events.umich.edu/event/147754
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:ecology,Bsbsigns,Ecology & Biology,Ecology And Evolutionary Biology,eeb,seminar
LOCATION:Biological Sciences Building - 5150
CONTACT:
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DTSTAMP:20260413T082805
DTSTART;TZID=America/Detroit:20260422T143000
DTEND;TZID=America/Detroit:20260422T155000
SUMMARY:Workshop / Seminar:Development as Skills and Altruism
DESCRIPTION:This paper emphasizes the central role of skill and altruism in development\, defined as an increase in social welfare. In the basic model\, skills expand the set of feasible payoffs\, while altruism guides the decision maker’s choice. Greater skills need not increase social welfare\, because such expansions combine a positive frontier effect with an ambiguous substitution effect\, potentially toward actions that are privately attractive but socially harmful. This ambiguous effect of skills on development is referred to as the “lottery of the technology” and disappears when altruism is high enough to ensure that new opportunities created by skills are used only when they raise social welfare. Extensions of the model show that 1) endogenous technological change makes altruism even more influential in the long run 2) the effect of stronger institutions is subject to a “lottery of alignment of interests” between policymakers’ private gains and social welfare\, unless policymakers’ altruism is high enough to ensure the good use of institutional power\, making institutions a lever of altruism rather than a substitute and 3) allowing altruism to be group-specific shows that only universal altruism guarantees the effective use of skill improvements. This framework speaks to many contemporary and historical events and leads to the conclusion that ensuring long-term development requires both the selection of altruistic leaders and a population shift in the distribution of altruism.
UID:144125-21894695@events.umich.edu
URL:https://events.umich.edu/event/144125
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Economics,seminar,Development
LOCATION:North Quad - 4300
CONTACT:
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