BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Claremont Center for the Mathematical Sciences - ECPv6.15.17.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Claremont Center for the Mathematical Sciences X-ORIGINAL-URL:https://colleges.claremont.edu/ccms X-WR-CALDESC:Events for Claremont Center for the Mathematical Sciences REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250428T161500 DTEND;TZID=America/Los_Angeles:20250428T171500 DTSTAMP:20260430T141121 CREATED:20250426T011634Z LAST-MODIFIED:20250426T011735Z UID:3772-1745856900-1745860500@colleges.claremont.edu SUMMARY:Applied Math Seminar: Alejandra Castillo (Pomona College) DESCRIPTION:Title:    Randomized Kaczmarz Methods for Corrupted Tensor Linear Systems \nAbstract: Recovering tensor-valued signals from corrupted measurements is a central problem in various applications such as hyperspectral image reconstruction and medical imaging. This talk considers tensor linear systems of the form AX = B\, that contain observations potentially affected by sparse\, large-magnitude corruptions.  A quantile-based randomized Kaczmarz algorithm\, called quantile tensor randomized Kaczmarz (QTRK)\, is discussed to address this challenge. By integrating quantile statistics into the iterative update process\, QTRK improves robustness against adversarial errors. A variant selectively omits unreliable measurements to enhance stability further. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-alejandra-castillo-pomona-college/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250421T161500 DTEND;TZID=America/Los_Angeles:20250421T171500 DTSTAMP:20260430T141121 CREATED:20250130T003350Z LAST-MODIFIED:20250320T010652Z UID:3671-1745252100-1745255700@colleges.claremont.edu SUMMARY:Applied Math Seminar: Fabio Milner (Arizona State University) DESCRIPTION:Title: Modeling viral STI epidemics \nAbstract: We will describe an SIR model of viral sexually transmitted infections in a population structured by sex and sexual preference and its validation in the simple SI case from HIV data incidence. We will also use the model to establish a plausible structure of the U.S. population by sexual preferences for men and women and perform a case scenario analysis of the impact that changes in sexual preferences may have in the incidence of STIs. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-fabio-milner-arizona-state-university/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250414T161500 DTEND;TZID=America/Los_Angeles:20250414T171500 DTSTAMP:20260430T141121 CREATED:20250130T003204Z LAST-MODIFIED:20250401T164506Z UID:3670-1744647300-1744650900@colleges.claremont.edu SUMMARY:Applied Math Seminar: Victoria Chebotaeva (USC) DESCRIPTION:Title: Erlang-Distributed SEIR Epidemical Models \nAbstract: \nWe examine the effects of different dynamics in epidemiological models\, focusing on two key approaches. The first model incorporates reaction-diffusion dynamics\, where susceptible individuals avoid areas with high concentrations of infected individuals. The second model divides exposed and infectious individuals into symptomatic and asymptomatic subclasses.\nOur findings emphasize the importance of adaptive control measures\, such as targeted testing\, contact tracing\, and isolation\, in effectively containing disease spread while minimizing societal and economic impacts. The models highlight the distinct roles of symptomatic and asymptomatic individuals\, demonstrating how tailored public health strategies can improve resource management and mitigate the socio-economic effects of outbreaks. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-course-preview-2/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250407T161500 DTEND;TZID=America/Los_Angeles:20250407T171500 DTSTAMP:20260430T141121 CREATED:20250402T004832Z LAST-MODIFIED:20250402T004832Z UID:3749-1744042500-1744046100@colleges.claremont.edu SUMMARY:Applied Math Seminar: Ethan Epperly (Caltech) DESCRIPTION:Title: Randomly pivoted Cholesky: Fast\, accurate matrix approximation for scientific machine learning \nAbstract:\nLow-rank approximation of positive semidefinite matrices is a basic problem in computational mathematics\, with many applications to machine learning and scientific computing. Existing approaches for this problem largely fall into two categories: simple\, fast\, but sometimes inaccurate methods and sophisticated\, slower methods with accuracy guarantees. To achieve the best of both worlds\, this talk introduces randomly pivoted Cholesky\, an algorithm for positive semidefinite low-rank approximation that is simple\, fast\, and accurate. We demonstrate the effectiveness of randomly pivoted Cholesky for spectral clustering of molecular dynamics data\, achieving an order of magnitude lower clustering error than previous methods. We then go on to discuss theoretical guarantees for randomly pivoted Cholesky. Using a matrix concavity argument\, we show that randomly pivoted Cholesky has nearly optimal low-rank approximation properties. We conclude by discussing extensions and future prospects for this simple\, yet effective\, algorithm. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-ethan-epperly-caltech/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250324T161500 DTEND;TZID=America/Los_Angeles:20250324T171500 DTSTAMP:20260430T141121 CREATED:20250130T190206Z LAST-MODIFIED:20250311T055012Z UID:3672-1742832900-1742836500@colleges.claremont.edu SUMMARY:Applied Math Seminar: Ryan O'Dowd (Claremont Graduate University) DESCRIPTION:Title: Learning on manifolds without manifold learning \nAbstract: Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampled from an unknown submanifold of a high dimensional Euclidean space. A great deal of research deals with obtaining information about this manifold\, such as the eigendecomposition of the Laplace-Beltrami operator or coordinate charts\, and using this information for function approximation. This two-step approach implies some extra errors in the approximation stemming from estimating the basic quantities of the data manifold in addition to the errors inherent in function approximation. In this paper\, we project the unknown manifold as a submanifold of an ambient hypersphere and study the question of constructing a one-shot approximation using a specially designed sequence of localized spherical polynomial kernels on the hypersphere. Our approach does not require preprocessing of the data to obtain information about the manifold other than its dimension. We give optimal rates of approximation for relatively “rough” functions. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-ryan-odowd-claremont-graduate-university/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250310T161500 DTEND;TZID=America/Los_Angeles:20250310T171500 DTSTAMP:20260430T141121 CREATED:20250130T002924Z LAST-MODIFIED:20250303T200907Z UID:3668-1741623300-1741626900@colleges.claremont.edu SUMMARY:Applied Math Seminar: Sarah Robinson (Claremont McKenna College) DESCRIPTION:Title: Do Taxes Affect Pre-Tax Income Inequality? Evidence from 100 Years of U.S. States \nAbstract: We study how U.S. state taxes have affected pre-tax income inequality during the last century. Our primary analysis focuses on the top marginal personal income and corporate income tax\, and their effect on top incomes and top income shares within each state. The long panel nature of our data\, from 1917 to 2018\, allows us to study the effect of tax adoptions\, tax cancellations\, and tax changes\, and furthermore to assess both immediate and long-term relationships. With event study and synthetic control designs\, we generally find no statistically significant relationship between tax measures and inequality. Some of our point estimates\, as well as a simple two-way fixed effects analysis\, suggest that higher income taxes may reduce top incomes and income shares. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-sarah-robinson-claremont-mckenna-college/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250303T161500 DTEND;TZID=America/Los_Angeles:20250303T171500 DTSTAMP:20260430T141121 CREATED:20250130T002829Z LAST-MODIFIED:20250225T061553Z UID:3666-1741018500-1741022100@colleges.claremont.edu SUMMARY:Applied Math Seminar: Efstratios Tsoukanis (Claremont Graduate University) DESCRIPTION:Title: Bi-Lipschitz Invariants \nAbstract: Consider a finite-dimensional real vector space and a finite group acting unitarily on it. We investigate the general problem of constructing Euclidean stable embeddings of the quotient space of orbits. Our embedding relies on subsets of sorted coorbits with respect to chosen window vectors. Our main injectivity results examine the conditions under which such embeddings are injective. We establish these results using semialgebraic techniques. Furthermore\, our main stability result states and demonstrates that any embedding based on sorted coorbits is automatically bi-Lipschitz when injective. We establish this result using geometric function techniques. Our work has applications in data science\, where certain systems exhibit intrinsic invariance to group actions. For instance\, in graph deep learning\, graph-level regression and classification models must be invariant to node labeling. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-efstratios-tsoukanis-claremont-graduate-university/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250224T161500 DTEND;TZID=America/Los_Angeles:20250224T171500 DTSTAMP:20260430T141121 CREATED:20250214T080219Z LAST-MODIFIED:20250214T080318Z UID:3698-1740413700-1740417300@colleges.claremont.edu SUMMARY:Applied Math Seminar: Ryan Aschoff (UC Riverside) DESCRIPTION:Title: Smooth non-decaying solutions to the 2D dissipative quasi-geostrophic equations \nAbstract: In this talk we explore the two-dimensional dissipative surface quasi-geostrophic (SQG) equation with fractional diffusion of order 2α for α ∈ (1/2\,1]\, focusing on the setting where the initial data does not decay at spatial infinity and periodicity is not assumed. In geophysical applications\, the equations model shallow water currents with the scalar field θ is interpreted as the pressure\, while the associated velocity field u governs the fluid motion. Traditionally\, the transport velocity is recovered from the pressure via a constitutive law that fails when decay is absent. To overcome this\, we replace it with a generalized\, Serfati-type constitutive law—a method originally developed for the 2D Euler equations. \nWe will discuss how this approach enables us to prove the global existence and uniqueness of mild solutions\, as well as classical solutions (with data bounded in C^k\, for k≥2) without relying on spatial decay. The presentation will include an overview of the reformulated mild solution framework\, which couples the pressure and velocity equations via the fractional heat operator and a modified convolution structure. In addition\, we will outline extensions of this method to a Serfati-type SQG system and indicate how Littlewood-Paley techniques can be used to approach the inviscid case. URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-ryan-aschoff-uc-riverside/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250217T161500 DTEND;TZID=America/Los_Angeles:20250217T171500 DTSTAMP:20260430T141121 CREATED:20250130T002710Z LAST-MODIFIED:20250210T172352Z UID:3665-1739808900-1739812500@colleges.claremont.edu SUMMARY:Applied Math Seminar: Denis Gaidashev (Uppsala University) DESCRIPTION:Title: Renormalization and wild attractors for Fibonacci maps \nAbstract: A Fibonacci map is a piecewise defined map of a subset of an interval I onto I with a unique critical point of order d whose orbit undergoes nearest returns at Fibonacci times. It has been shown by Bruin\, Keller\, Nowicki and van Strien that such maps exhibit “wild” attractors: Cantor sets of zero Lebesgue measure whose basin of attraction is meager but has positive Lebesgue measure. We will discuss real renormalization\, and a trichotomy for Fibonacci maps\, similar to the Avila-Lyubich trichotomy for Feigenbaum Julia sets\, which\, in particular\, allows us to show that Fibonacci maps admit wild attractors for d=5.1\, and do not for d=3.9 (and\, conjecturally\, for 2