• A Signal Separation View of Classification (Ryan O’Dowd, CGU)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    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 […]

  • Applied Math Seminar: Christina Edholm (Scripps College)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: Controlling the unmanageable: insight into control methods for biological systems Abstract: When formulating a model for a biological system, often we want to use the model to understand the […]

  • Dagan Karp (Harvey Mudd College)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: Tropical Linear Series Abstract: In this talk I'll attempt to give a friendly and example-driven introduction to the theory of linear series on tropical curves. While in some respects mirroring the classical study of linear series, in the tropical setting there are many surprises and even basic questions remain open. This work is joint with […]

  • Applied Math Seminar: Adam Yassine (Pomona College)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: On the Composition of Classical Mechanical Systems Abstract: Compositionality is a basic principle for understanding the physical world. The underlying idea is to study a system by studying the ways in which the components of the system compose to form the system. Category theory is an area in mathematics that is particularly well-suited for […]

  • Applied Math Seminar: Claremont Colleges Course Previews for Spring 2024

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    During this student-centered Applied Math Seminar, there will be discussion and presentations about upcoming courses offered in applied mathematics, to help students make their enrollment choices for Spring 2024 and […]

  • Applied Math Seminar: Jeremy Brandman (DCS corporation)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title Control algorithms for unmanned underwater vehicles: new approaches based on Hamilton-Jacobi equations and reinforcement learning. Abstract Unmanned underwater vehicles (UUVs) are defined by their ability to operate without direct […]

  • Applied Math Seminar: Evan Rosenman (CMC)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title:  Recalibration of Predicted Probabilities Using the "Logit Shift": Why Does It Work, and When Can It Be Expected to Work Well? Abstract: In the context of election analysis, researchers […]

  • Applied Math Seminar: Dan Pirjol (Stevens Institute of Technology)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: The Hartman-Watson distribution: numerical evaluation and applications in mathematical finance Abstract: The Hartman-Watson distribution appears in several problems of applied probability and financial mathematics. Most notably, it determines the […]

  • Applied Math Seminar: Tin Thien Phan (Los Alamos National Laboratory)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: Understanding SARS-CoV-2 viral rebounds with and without treatments. Abstract: 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 […]

  • Applied Math Seminar: Michael Murray (UCLA)

    Estella 1021 (Emmy Noether Room), Pomona College Claremont, CA, United States

    Title: Towards Understanding the Success of First Order Methods in Training Mildly Overparameterized Networks Abstract: 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, […]