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DTSTART;TZID=America/Los_Angeles:20231009T161500
DTEND;TZID=America/Los_Angeles:20231009T171500
DTSTAMP:20260421T025908
CREATED:20230912T154505Z
LAST-MODIFIED:20231004T015412Z
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SUMMARY:Applied Math Seminar: Dan Pirjol (Stevens Institute of Technology)
DESCRIPTION:Title: The Hartman-Watson distribution: numerical evaluation and applications in mathematical finance \nAbstract: The Hartman-Watson distribution appears in several problems of applied probability and financial mathematics. Most notably\, it determines the joint distribution of the time-integral of a geometric Brownian motion and its terminal value. A classical result by Yor (1981) expresses it as a one-dimensional integral which is however difficult to evaluate numerically in the region of interest for financial applications. The talk gives an introduction to the HW distribution and presents an asymptotic expansion which can be used for an efficient numerical evaluation. Two applications from mathematical finance are discussed: Asian options pricing in the Black-Scholes model\, and option pricing in the log-normal SABR model.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-dan-pirjol-stevens-institute-of-technology/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ami Radunskaya":MAILTO:aradunskaya@pomona.edu
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DTSTART;TZID=America/Los_Angeles:20231002T161500
DTEND;TZID=America/Los_Angeles:20231002T171500
DTSTAMP:20260421T025908
CREATED:20230912T154332Z
LAST-MODIFIED:20230929T050112Z
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SUMMARY:Applied Math Seminar: Tin Thien Phan (Los Alamos National Laboratory)
DESCRIPTION:Title: Understanding SARS-CoV-2 viral rebounds with and without treatments. \nAbstract: In most instances\, the characteristics of SARS-CoV-2 mirror the patterns of an acute infection\, with viral load rapidly peaking around 5 days post-infection and subsequently clearing within 2 weeks. However\, some individuals show signs of viral recrudescence of up to 10000 viral RNA copies/mL shortly following viral remission. These instances of viral resurgence\, distinct from long COVID\, are generally resolved within four weeks post-infection and have been observed across varying treatment statuses\, vaccination statuses\, and viral strains. In this presentation\, I will review existing evidence of transient viral rebound and demonstrate that a class of dynamic models that incorporates virus-immune interaction accurately describes transient viral rebound dynamics under different treatment scenarios\, including those untreated. While these models all share a simple structure with a unique globally-asymptomatic-stable disease-free equilibrium\, the most exciting and relevant aspect hides within their transient phase and remains largely unexplored.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-tin-thien-phan-los-alamos-national-laboratory/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ami Radunskaya":MAILTO:aradunskaya@pomona.edu
END:VEVENT
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DTSTART;TZID=America/Los_Angeles:20230918T161500
DTEND;TZID=America/Los_Angeles:20230918T171500
DTSTAMP:20260421T025908
CREATED:20230912T153609Z
LAST-MODIFIED:20230912T162421Z
UID:3198-1695053700-1695057300@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Michael Murray (UCLA)
DESCRIPTION:Title: Towards Understanding the Success of First Order Methods in Training Mildly Overparameterized Networks \nAbstract: For most problems of interest the loss landscape of a neural network is non-convex and contains a plethora of spurious critical points. Despite this first order methods such as SGD and Adam are in practice remarkably successful at finding optimal\, or at the least near optimal\, minimizers of the loss. In recent years the Neural Tangent Kernel has proven a powerful tool in explaining this phenomena and for providing guarantees for highly overparameterized networks. However\, for mildly overparameterized networks (where width scales linearithmically in the sample size) where richer feature learning can occur an explanation is lacking. In this talk I will present recent results on the loss landscape of two-layer mildly overparameterized ReLU networks. Our approach involves bounding the dimension of the sets of local and global minima using the rank of the Jacobian of the parameterization map. Using results on random binary matrices\, we show most activation patterns correspond to parameter regions with no bad differentiable local minima. Furthermore\, for one-dimensional input data\, we show most activation regions realizable by the network contain a high dimensional set of global minima and no bad local minima. We experimentally confirm these results by finding a phase transition from most regions having full rank to many regions having deficient rank depending on the amount of overparameterization.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-michael-murray-ucla/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ami Radunskaya":MAILTO:aradunskaya@pomona.edu
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