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-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 BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20270314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20271107T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260126T160000 DTEND;TZID=America/Los_Angeles:20260126T170000 DTSTAMP:20260427T185904 CREATED:20260121T185658Z LAST-MODIFIED:20260121T185658Z UID:3967-1769443200-1769446800@colleges.claremont.edu SUMMARY:Fractional Brownian Motion: Small Increments and First Exit Time from One-sided Barrier (Qidi Peng\, CGU) DESCRIPTION:Abstract: The talk introduces a conjecture on the first exit time of fractional Brownian motion: the upper-tail probability for a fractional Brownian motion to first exit a positive-valued barrier over time T has the exact asymptotic rate T^(H-1)\, where H is the Hurst parameter of the fractional Brownian motion. The talk tries to understand this conjecture by providing several equivalent statements. We then introduce the best effort made in the current literature towards solving this conjecture. URL:https://colleges.claremont.edu/ccms/event/fractional-brownian-motion-small-increments-and-first-exit-time-from-one-sided-barrier-qidi-peng-cgu/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251201T161500 DTEND;TZID=America/Los_Angeles:20251201T171500 DTSTAMP:20260427T185904 CREATED:20251126T233248Z LAST-MODIFIED:20251126T233248Z UID:3935-1764605700-1764609300@colleges.claremont.edu SUMMARY:Structure-Aware Adaptive Nonconvex Optimization for Deep Learning and Scientific Computing (Minxin Zhang\, UCLA) DESCRIPTION:Abstract: Modern machine learning and scientific computing pose optimization challenges of unprecedented scale and complexity\, demanding fundamental advances in both theory and algorithmic design for nonconvex optimization. This talk presents recent advances that address these challenges by exploiting matrix and tensor structures\, integrating adaptivity\, and leveraging sampling techniques. In the first part\, I introduce AdaGO\, a new optimizer that combines orthogonalized momentum updates with adaptive learning rates. Building on the recent success of the Muon optimizer in large language model training\, AdaGO incorporates an AdaGrad-type stepsize that scales orthogonalized update directions by accumulated past gradient norms. This design preserves the structural advantage of orthogonalized updates while adapting stepsizes to noise and the optimization landscape. We establish optimal convergence rates for smooth nonconvex functions and demonstrate improved performance over Muon and Adam on classification and regression tasks. The second part focuses on zeroth-order global optimization. We develop a theoretical framework for inexact proximal point (IPP) methods for global optimization\, establishing convergence guarantees when proximal operators are estimated either deterministically or stochastically. The quadratic regularization in the proximal operator induces a concentrated Gibbs measure landscape that facilitates effective sampling. We propose two sampling-based algorithms: TT-IPP\, which constructs a low-rank tensor-train (TT) approximation using a randomized TT-cross algorithm\, and MC-IPP\, which employs Monte Carlo integration. Both IPP algorithms adaptively balance efficiency and accuracy in proximal operator estimation\, achieving strong performance across diverse benchmark functions and applications. Together\, these works advance structure-aware adaptive first-order optimization for deep learning and zeroth-order global optimization in scientific computing. URL:https://colleges.claremont.edu/ccms/event/structure-aware-adaptive-nonconvex-optimization-for-deep-learning-and-scientific-computing-minxin-zhang-ucla/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251117T161500 DTEND;TZID=America/Los_Angeles:20251117T171500 DTSTAMP:20260427T185904 CREATED:20251111T194006Z LAST-MODIFIED:20251111T194015Z UID:3924-1763396100-1763399700@colleges.claremont.edu SUMMARY:A Signal Separation View of Classification (Ryan O'Dowd\, CGU) DESCRIPTION:Abstract: The problem of classification in machine learning has often been approached in terms of function approximation. In this talk\, we propose an alternative approach for classification in arbitrary compact metric spaces which\, in theory\, yields both the number of classes\, and a perfect classification using a minimal number of queried labels. Our approach uses localized trigonometric polynomial kernels initially developed for the point source signal separation problem in signal processing. Rather than point sources\, we examine a convex combination of probability distributions representing the various classes from the machine learning classification problem. The localized kernel technique developed for separating point sources is then shown to separate the supports of these distributions. This is done in a hierarchical manner in our MASC algorithm to accommodate touching/overlapping class boundaries. The algorithm works in an active learning paradigm\, deciding on points to query for their true class label and extending those labels to nearby points. We illustrate our theory on several simulated and real life data sets\, including the Salinas and Indian Pines hyperspectral data sets and a document data set. URL:https://colleges.claremont.edu/ccms/event/a-signal-separation-view-of-classification-ryan-odowd-cgu/ LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251110T161500 DTEND;TZID=America/Los_Angeles:20251110T171500 DTSTAMP:20260427T185904 CREATED:20251006T190434Z LAST-MODIFIED:20251006T223009Z UID:3883-1762791300-1762794900@colleges.claremont.edu SUMMARY:To Wait or Not to Wait? A Trade-off Between Population Externality and Signal Quality (Lan-Yi Liu\, National Taiwan University) DESCRIPTION:Abstract: Transparency is vital for efficiency in social systems\, yet individuals with critical information often strategically postpone disclosure\, even when required\, to benefit themselves.\nTo study this behavior\, we introduce a multi-stage Chinese restaurant game with incomplete information that features system-recommended action rules and varying levels of player foresight. In our model\, players initially receive a suggestion to join a queueing group based on their private signal\, but can choose to switch groups. Following this\, players sequentially select a final resource\, balancing the desire to avoid congested externalities with the need to acquire more information.\nWe prove a closed-form solution for the players’ pure-strategy Nash equilibrium. Our key finding is that players with high-quality signals have no incentive to reveal their information to those with low-quality signals. This suggests that allowing players to strategically determine their decision timing\, without further system design\, leads to an inefficient equilibrium allocation.\nOur results on congested externalities and system suggestions help explain the inherent trade-off between information quality and decision timing in various real-world scenarios\, such as the challenges of vaccine distribution during a pandemic\, the strategic crowding of factory location selection\, and the decision-making faced by political candidates positioning themselves on the spectrum. URL:https://colleges.claremont.edu/ccms/event/to-wait-or-not-to-wait-a-trade-off-between-population-externality-and-signal-quality-lan-yi-liu-harvey-mudd-college/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251103T161500 DTEND;TZID=America/Los_Angeles:20251103T171500 DTSTAMP:20260427T185904 CREATED:20251021T180716Z LAST-MODIFIED:20251021T180716Z UID:3909-1762186500-1762190100@colleges.claremont.edu SUMMARY:Convergence analysis of the Alternating Anderson-Picard method for nonlinear fixed-point problems (Xue Feng\, UCLA) DESCRIPTION:Abstract: Anderson Acceleration (AA) has been widely used to solve nonlinear fixed-point problems due to its rapid convergence. This talk focuses on a variant of AA in which multiple Picard iterations are performed between each AA step\, referred to as the Alternating Anderson-Picard (AAP) method. Despite introducing more `slow’ Picard iterations\, this method has been demonstrated to be efficient and even more robust in both linear and nonlinear cases. However\, there is a lack of theoretical analysis for AAP in the nonlinear context. In this work\, we address this gap by establishing the equivalence between AAP and a multisecant-GMRES method that employs GMRES to solve a multisecant linear system at each iteration. From this perspective\, we show that AAP actually “converges” the well-known Newton-GMRES method. These connections also help us understand the convergence behavior of AAP\, especially the asymptotic convergence rate. URL:https://colleges.claremont.edu/ccms/event/convergence-analysis-of-the-alternating-anderson-picard-method-for-nonlinear-fixed-point-problems-xue-feng-ucla/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251027T161500 DTEND;TZID=America/Los_Angeles:20251027T171500 DTSTAMP:20260427T185904 CREATED:20251006T191634Z LAST-MODIFIED:20251006T191634Z UID:3885-1761581700-1761585300@colleges.claremont.edu SUMMARY:Estimating Shapley Values for Explainable AI via Richer Model Approximations (Teal Witter\, CMC) DESCRIPTION:Abstract: Modern machine learning is ultimately a simple process: We iteratively update the weights of machine learning models to minimize a problem-specific loss. When it works well\, we deploy the model in human-facing domains like healthcare\, finance\, or the justice system. But even though we know how models are trained\, we don’t understand why they make decisions the decision they do. A particularly compelling approach to explaining AI predictions is the Shapley value\, a game-theoretic quantity that measures how each input to the model affects its output. Mathematically\, the ith Shapley value is the average change in the ith dimension of a particular function defined on the d-dimensional hypercube. Because the hypercube has 2^d points\, exactly computing Shapley values is infeasible. In this talk\, we will instead leverage algorithmic insights to develop state-of-the-art approximation methods. URL:https://colleges.claremont.edu/ccms/event/estimating-shapley-values-for-explainable-ai-via-richer-model-approximations-teal-witter-cmc/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20251020T161500 DTEND;TZID=America/Los_Angeles:20251020T171500 DTSTAMP:20260427T185904 CREATED:20250829T230854Z LAST-MODIFIED:20250902T231226Z UID:3805-1760976900-1760980500@colleges.claremont.edu SUMMARY:Some New Advances in Similarity-Based Predictive Modeling (Joel A. Dubin\, University of Waterloo) DESCRIPTION:Abstract: Earlier work has shown that similarity-based predictive models can improve upon predictive performance\, as compared to using the entire training data to help build models\, particular regarding model discrimination for binary responses. My collaborators and I have some updated results to share\, regarding similarity-based modeling for joint consideration of model calibration and discrimination\, as well as for dynamic prediction models. Properties of our methods will be investigated in comprehensive simulation studies\, and we will demonstrate the methods through separate analyses of a publicly-available intensive care unit (ICU) database. URL:https://colleges.claremont.edu/ccms/event/some-new-advances-in-similarity-based-predictive-modeling-joel-a-dubin-university-of-waterloo/ 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:20251006T161500 DTEND;TZID=America/Los_Angeles:20251006T171500 DTSTAMP:20260427T185904 CREATED:20251006T190122Z LAST-MODIFIED:20251006T190122Z UID:3881-1759767300-1759770900@colleges.claremont.edu SUMMARY:Modeling drug release for in vitro experiments (Minaya Villasana De Armas\, Universidad Simon Bolivar) DESCRIPTION:Abstract: It is common to use adjuvants in immunotherapeutic regimens to strengthen the immune response. However\, multiple dosages are required making it inconvenient for the patient. Hydrogels have been proposed as a vehicle to administer adjuvant and antigen in a sustained slow release thus reducing the need for re-administration. \nIn this instance\, we use experimental data for stability studies on two different thermosensitive pentablock hydrogels as well as release of three adjuvants suspended in the hydrogels or in PLGA nanoparticles. The goal is to model this release and provide a framework by which these models can describe the various release profiles efficiently. \n  URL:https://colleges.claremont.edu/ccms/event/modeling-drug-release-for-in-vitro-experiments-minaya-villasana-de-armas-universidad-simon-bolivar/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250929T161500 DTEND;TZID=America/Los_Angeles:20250929T171500 DTSTAMP:20260427T185904 CREATED:20250922T153239Z LAST-MODIFIED:20250922T153239Z UID:3850-1759162500-1759166100@colleges.claremont.edu SUMMARY:Bounds and Extremal Examples for the Hot Spots Ratio (Alex Hsu\, University of Washington) DESCRIPTION:Abstract: The shape of the fluctuations as heat approaches equilibrium in an insulated body are governed by the first Neumann eigenfunction of the Laplacian. Rauch’s hot spots conjecture states that the extrema of the first nontrivial Neumann Laplacian eigenfunction for a Lipschitz domain lies on the boundary. While this conjecture is false in general\, its failure can be measured by the hot spots ratio\, defined as the maximum over the entire domain divided by the maximum on the boundary. We determine the supremum of this quantity over all Lipschitz domains in every dimension $d$ and construct a sequence of sets for which the hot spots ratio approach this supremum. As $d\to \infty$\, this maximal ratio converges to $\sqrt{e}$\, which matches the previously best known upper bounds. URL:https://colleges.claremont.edu/ccms/event/bounds-and-extremal-examples-for-the-hot-spots-ratio-alex-hsu-university-of-washington/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250915T161500 DTEND;TZID=America/Los_Angeles:20250915T171500 DTSTAMP:20260427T185904 CREATED:20250829T233516Z LAST-MODIFIED:20250922T153423Z UID:3811-1757952900-1757956500@colleges.claremont.edu SUMMARY:LA City Council Reform: A Statistical Study of Alternatives (Evan Rosenman & Sarah Cannon\, Claremont McKenna College) DESCRIPTION:Abstract: \nThe 2022 Los Angeles City Council scandal intensified public demand for governance reform\, leading to the creation of the Los Angeles Charter Reform Commission. The commission is now considering proposals from civic and academic groups. Major recommendations include: eliminating the automatic election of candidates who win a primary majority\, expanding the size of the City Council\, and adopting alternative electoral systems such as multimember districts and ranked-choice voting. \nThis project offers a rigorous\, data-driven evaluation of these proposals\, focusing on their implications for proportionality\, racial representation\, and electoral responsiveness. We combine methods from Statistics and Computer Science\, including Bayesian ethnicity imputation\, ecological inference\, and advanced graph-sampling algorithms to explore district boundaries. This hybrid approach provides new insights into Los Angeles’s political geography and the challenges of building a fair\, representative City Council. By providing empirical evidence on the strengths and weaknesses of various districting systems\, our work aims to inform policymaking and advance democratic representation in Los Angeles. URL:https://colleges.claremont.edu/ccms/event/la-city-council-reform-a-statistical-study-of-alternatives-evan-rosenman-claremont-mckenna-college/ LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250908T161500 DTEND;TZID=America/Los_Angeles:20250908T171500 DTSTAMP:20260427T185904 CREATED:20250829T233038Z LAST-MODIFIED:20250902T231307Z UID:3810-1757348100-1757351700@colleges.claremont.edu SUMMARY:The Shooting Method in the Analysis of Two-Point Boundary-Value Problems (Adolfo J. Rumbos\, Pomona College) DESCRIPTION:Abstract: \nTwo-point boundary-value problems (BVPs) appear frequently in applied mathematics.  When looking for solutions of boundary-value problems for some partial differential equations (PDEs) in mathematical physics\, two-point BVPs come up as a result of applying the method of separation of variables\, for instance. In the case of linear PDEs\, the resulting two-point BVPs fall into a class of problems known as Sturm-Liouville eigenvalue problems. \nThis presentation deals with the use of the shooting method to prove existence of solutions of two-point BVPs.  The shooting method is a numerical technique used to estimate solutions of two-point BVPs once a solution is known to exist.  In this talk we illustrate how the shooting method can be used to prove existence of eigenvalues of linear Sturm-Liouville problems.  We also show how the shooting method can be applied to prove existence and uniqueness of solutions for some nonlinear\, two-point BVPs\, and existence of eigenvalues for some nonlinear eigenvalue problems. \nThe presentation describes research conducted with collaborators Vaidehi Srinivasan (Pomona College class of 2027) and Gavin Zhao (Pomona College class of 2029) in the summer of 2025 with the support of the Summer Undergraduate Research Program at Pomona College. URL:https://colleges.claremont.edu/ccms/event/the-shooting-method-in-the-analysis-of-two-point-boundary-value-problems-adolfo-j-rumbos-pomona-college/ CATEGORIES:Applied Math Seminar ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250505T161500 DTEND;TZID=America/Los_Angeles:20250505T171500 DTSTAMP:20260427T185904 CREATED:20250130T003036Z LAST-MODIFIED:20250327T174327Z UID:3669-1746461700-1746465300@colleges.claremont.edu SUMMARY:Applied Math Seminar: Evan Rosenman (Claremont McKenna College) DESCRIPTION:Title: TBA \nAbstract: TBA URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-evan-rosenman-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:20250428T161500 DTEND;TZID=America/Los_Angeles:20250428T171500 DTSTAMP:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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:20260427T185904 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