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:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231002T161500
DTEND;TZID=America/Los_Angeles:20231002T171500
DTSTAMP:20260623T062716
CREATED:20230912T154332Z
LAST-MODIFIED:20230929T050112Z
UID:3201-1696263300-1696266900@colleges.claremont.edu
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
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231009T161500
DTEND;TZID=America/Los_Angeles:20231009T171500
DTSTAMP:20260623T062716
CREATED:20230912T154505Z
LAST-MODIFIED:20231004T015412Z
UID:3202-1696868100-1696871700@colleges.claremont.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231023T161500
DTEND;TZID=America/Los_Angeles:20231023T171500
DTSTAMP:20260623T062716
CREATED:20230912T154613Z
LAST-MODIFIED:20231023T033921Z
UID:3203-1698077700-1698081300@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Evan Rosenman (CMC)
DESCRIPTION:Title:  Recalibration of Predicted Probabilities Using the “Logit Shift”: Why Does It Work\, and When Can It Be Expected to Work Well? \nAbstract: In the context of election analysis\, researchers frequently face the “recalibration problem.” That is: they must reconcile individual-level vote probabilities\, modeled prior to the election\, with vote totals observed in each precinct once the election has taken place. Making these adjustments such that the probabilities match known aggregates\, researchers can obtain better-calibrated estimates of key quantities such as vote preferences among subgroups of the electorate defined by race\, age\, and gender. \nWe provide theoretical grounding for one of the most commonly used recalibration strategies\, known colloquially as the “logit shift.” The logit shift is a heuristic adjustment\, in which a constant correction on the logit scale is found\, such that aggregated predictions match observed totals. \nWe show that the logit shift offers a fast and accurate approximation to a principled\, but computationally impractical adjustment strategy: computing the posterior probabilities\, conditional on the observed totals. After deriving analytical bounds on the quality of the approximation\, we illustrate its accuracy using Monte Carlo simulations. We also discuss scenarios in which the logit shift is less effective at recalibrating predictions: when the totals are available only for highly heterogeneous populations\, and when the original predictions correctly capture the mean of true individual probabilities\, but fail to capture the shape of their distribution.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-evan-rosenman-cmc/
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
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231030T161500
DTEND;TZID=America/Los_Angeles:20231030T173000
DTSTAMP:20260623T062716
CREATED:20231019T153510Z
LAST-MODIFIED:20231023T034142Z
UID:3292-1698682500-1698687000@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Jeremy Brandman (DCS corporation)
DESCRIPTION:Title \nControl algorithms for unmanned underwater vehicles: new approaches based on Hamilton-Jacobi equations and reinforcement learning. \nAbstract \nUnmanned underwater vehicles (UUVs) are defined by their ability to operate without direct human intervention.  As a result\, UUVs are valuable for surveillance tasks\, especially in the presence of hazardous environmental conditions. Specific applications of UUVs include seafloor mapping\, mine detection\, and oil pipeline inspection. \nIn this talk\, we propose new algorithms for two aspects of UUV control: path planning and vehicle guidance.  Path planning identifies a vehicle trajectory\, based on anticipated environmental conditions\, that achieves desired mission objectives (e.g. obstacle avoidance\, minimization of energy consumption). Vehicle guidance responds to observed environmental conditions in order to maintain fidelity to the path selected by the path planner. \nThe first half of this talk considers a new approach to path planning based on solving Hamilton-Jacobi partial differential equations (PDE).  The starting point for this method is the observation that the vehicle’s minimum travel-time satisfies a time-independent Hamilton-Jacobi equation .  Numerical solutions to this PDE are efficiently computed using the fast sweeping method.  Our approach is validated through several examples for which optimal trajectories are derived using the calculus of variations. \nThe second half of this talk introduces a reinforcement learning framework for incorporating in situ ocean current measurements into the guidance system in an energetically optimal manner.  Scaling and symmetry considerations turn out to play an important role in the framework’s efficiency and robustness.  Numerical results demonstrate that the energetic cost of transits executed under the guidance of a trained agent approaches optimal performance.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-jeremy-brandman/
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
END:VCALENDAR