Fall 2025
Time & Location: Typically talks will be in Gibson Hall at 3:00 pm on a Friday.
Organizers: Chen, Hongfei and Gkogkou, Aikaterini
October 10, 2025Title: TBA
Speaker: Nick Cogan - Florida State University
Abstract: TBA
Time: TBA PMLocation: TBA
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October 17, 2025
Title: Extreme Superposition: Rogue Waves of Infinite Order, Universality, and Anomalous Temporal Decay
Speaker: Deniz Bilman - the University of Cincinnati (Host): Ken
Abstract: Focusing nonlinear Schrödinger equation serves as a universal model for the amplitude of a wave packet in a general one-dimensional weakly-nonlinear and strongly-dispersive setting that includes water waves and nonlinear optics as special cases. Rogue waves of infinite order are a novel family of solutions of the focusing nonlinear Schrödinger equation that emerge universally in a particular asymptotic regime involving a large-amplitude and near-field limit of a broad class of solutions of the same equation. In this talk, we will present several recent results on the emergence of these special solutions along with their interesting asymptotic and exact properties. Notably, these solutions exhibit anomalously slow temporal decay and are connected to the third Painlevé equation. Finally, we will extend the emergence of rogue waves of infinite order to the first several flows of the AKNS hierarchy—allowing for arbitrarily many simultaneous flows. Time permitting, we will report on recent work regarding their space-time asymptotic behavior under an arbitrary flow from the hierarchy.
Time: 3:00 PM
Location: GI 126A
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November 7, 2025
Title: TBA
Speaker: Prerona Dutta - Xavier University
Abstract: TBA
Time: TBA PM
Location: TBA
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November 14, 2024
Title: TBA
Speaker: Dana Ferranti - Worcester Polytechnic Institute (WPI)
Abstract: TBA
Time: TBA PM
Location: TBA
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December 05, 2025
Title: Scalable Multiclass High-Dimensional Linear Discriminant Analysis via the Randomized Kaczmarz Method
Speaker: Jocelyn Chi - University of Minnesota (Host): Xiang Ji
Abstract: Fisher's linear discriminant analysis (LDA) is a foundational method of dimension reduction for classification that has been useful in a wide range of applications. The goal is to identify an optimal subspace to project the observations onto that simultaneously maximizes between-group variation while minimizing within-group differences. The solution is straightforward when the number of observations is greater than the number of features but difficulties arise in the high dimensional setting, where there are more features than there are observations. Many works have proposed solutions for the high dimensional setting and frequently involve additional assumptions or tuning parameters. We propose a fast and simple iterative algorithm for high dimensional multiclass LDA on large data that is free from these additional requirements and that comes with some guarantees. We demonstrate our algorithm on real data and highlight some results.
Time: 3:00 PM
Location: Gibson Hall 126A
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