Title: Modeling size distributions and collisions in cloud microphysics Abstract: Feedbacks between a warming atmosphere, emission of aerosols, and clouds and precipitation are one of the most difficult aspects for climate models to accurately capture. While these models operate at resolutions of tens or hundreds of kilometers, many of the physics that determine how and […]
Title: PLSS: A Projected Linear Systems Solver (joint work with Michael Saunders) Abstract: Iteratively solving linear systems has proven to be useful for many large applications. Projection methods use sketching matrices (possibly randomized) to generate a sequence of small projected subproblems, but even the smaller systems can be costly. We develop a method in which […]
Title: A common pathway to cancer: oncogenic mutations abolish p53 oscillations. Abstract: The tumor suppressor p53 oscillates in response to DNA double-strand breaks, a behavior that has been suggested to be essential to its anti-cancer function. Nearly all human cancers have genetic alterations in the p53 pathway; a number of these alterations have been shown to […]
Title:Inferring birth and death rates from population size time series data Abstract: Models of population dynamics are usually formulated and analyzed with net growth rates. However, separately identifying birth and death rates is significant in various biological applications such as disambiguating (1) exploitation vs. interference competition in ecology, (2) bacteriostatic vs. bactericidal antibiotics in clinical […]
Deniz Sarikaya joining us from the Technical University of Denmark and speaking on "Narratives of Mathematical Practice (and why they matter!)" (abstract below). The speaker will join via zoom, but there will be a live audience on the second floor of Pitzer College's Gold Student Center in the Multipurpose room (in the building marked 3 here: https://www.pitzer.edu/about/maps-directions/quick-reference-map/). […]
Title: Identification of the order of semilinear subdiffusion with memory Abstract: See attached abstract
Title:Geometric Scattering on Measure Spaces Abstract: Geometric Deep Learning is an emerging field of research that aims to extend the success of convolutional neural networks (CNNs) to data with non-Euclidean geometric structure. Despitebeing in its relative infancy, this field has already found great success in many applications such as recommender systems, computer graphics, and traffic […]
