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        "event_title":"Revolutionary Paine: Andy Murphy Student-Curated Class Exhibit Common Sense",
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        "combined_title":"Revolutionary Paine: Andy Murphy Student-Curated Class Exhibit Common Sense",
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        "event_title":"The Regulatory, Property, and Human Rights-Based Strategies for Protecting American Waterways",
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        "description":"Erin Ryan, Associate Dean for Environmental Programs and Elizabeth C. & Clyde W. Atkinson Professor, Florida State University College of Law\r\n\r\nThis analysis introduces a framework of three different strategies for protecting American waterways\u2014the conventional regulatory approach, an alternative property-based approach, and a newer human rights-based approach\u2014and 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. \r\n\r\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.",
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        "event_title":"University Career Center Clothes Closet 10th Birthday Party",
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        "tags":["Athletics","Athletics - Women's Lacrosse"],
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        "event_title":"Xiaofeng Dai - Dissertation Defense",
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        "datetime_start":"20260422T130000",
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        "event_title":"Marjorie Lee Browne Scholars Mini-Symposium",
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        "combined_title":"Marjorie Lee Browne Scholars Mini-Symposium: Ian Augsburger, Ammar Eltigani, and Nicholas Simafranca",
        "event_subtitle":"Ian Augsburger, Ammar Eltigani, and Nicholas Simafranca",
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        "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\u2014most notably MCMC- and variational-based methods\u2014are 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\u2014where the number of samples and their dimensions grow proportionally\u2014is 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 Mar\u010denko\u2013Pastur 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.",
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