Author: boyarsky

Probability, Analysis, and Data Science (PADS) Seminar: Regime-Switching Jump-Diffusion Processes with Countable Regimes

Speaker: Chao Zhu (University of Wisconsin-Milwaukee) Abstract: This work focuses on a class of regime-switching jump diffusion processes, which is a two component Markov processes $(X(t),\Lambda(t))$, where the analog component $X(t) \in R^{d}$ models the state of interest while the switching component $\Lambda(t)\in \{1,2,\dots\}$ can be used to describe the structural changes of the state … Continue reading Probability, Analysis, and Data Science (PADS) Seminar: Regime-Switching Jump-Diffusion Processes with Countable Regimes

April 14, 2021, 4:10-5:00pm | Zoom

Mathematical Biology Seminar: Modeling gene regulatory network dynamics at single-cell resolution

Speaker: Adam MacLean, University of Southern California  https://g.co/kgs/5y6b2f Abstract: Since single-cell RNA sequencing technologies have become widespread, great efforts have been made to develop appropriate computational methods to learn biological features from high dimensional datasets. Much less effort has gone into the important yet challenging task of inferring dynamics from these genomic data. Here we … Continue reading Mathematical Biology Seminar: Modeling gene regulatory network dynamics at single-cell resolution

April 14, 2021, 3:20-4:10pm | Zoom

ISU Discrete Math Seminar: Empty axis-pallel boxes

Speaker: Boris Bukh (Carnegie Mellon University) How to place n points inside the d-dimensional unit cube so every large axis-parallel box contains at least one point? We discuss the motivation as well as a partial solution to this problem. This is a joint work with Ting-Wei Chao. To be added to the email list to … Continue reading ISU Discrete Math Seminar: Empty axis-pallel boxes

April 8, 2021, 2:10-3:00pm | Zoom 

Computational and Applied Mathematics (CAM) Seminar: First-order image restoration models for staircase reduction and contrast preservation

Speaker:  Wei Zhu (University of Alabama) In this talk, we will discuss two novel first-order variational models for image restoration. In the literature, lots of higher-order models were proposed to fix the staircase effect. In our first model, we consider a first-order variational model that imposes stronger regularity than total variation on regions with small … Continue reading Computational and Applied Mathematics (CAM) Seminar: First-order image restoration models for staircase reduction and contrast preservation

April 12, 2021, 4:10-5:00pm | Zoom

Probability, Analysis, and Data Science (PADS) Seminar: Rates of convergence to statistical equilibrium: a general approach and applications

Speaker: Cecilia Mondaini (Drexel University)   Randomness is an intrinsic part of many physical systems. For example, it might appear due to uncertainty in the initial data, or in the derivation of the mathematical model, or also in observational measurements. In this talk, we focus on the study of convergence/mixing rates for stochastic/random dynamical systems … Continue reading Probability, Analysis, and Data Science (PADS) Seminar: Rates of convergence to statistical equilibrium: a general approach and applications

April 7, 2021, 4:10-5:00pm | Zoom

Mathematics Colloquium: Anderson acceleration and how it speeds up convergence in fixed point iterations

Speaker:  Leo Rebholz (Clemson University)   Anderson acceleration (AA) is an extrapolation technique originally proposed in 1965 that recombines the most recent iterates and update steps in a fixed point iteration to improve the convergence properties of the sequence. Despite being successfully used for many years to improve nonlinear solver behavior on a wide variety of … Continue reading Mathematics Colloquium: Anderson acceleration and how it speeds up convergence in fixed point iterations

April 6, 2021, 3:10-4:00pm | Zoom

The Mathematics and Deep Learning Collective: Exploratory Data Analysis for Data Objects on a Metric Space via Tukey’s Depth

Exploratory data analysis involves looking at the data and understanding what can be done with them. Non-standard data objects such as directions, covariance matrices, trees, functions, and images have become increasingly common in modern practice. Such complex data objects are hard to examine due to the lack of a natural ordering and efficient visualization tools. … Continue reading The Mathematics and Deep Learning Collective: Exploratory Data Analysis for Data Objects on a Metric Space via Tukey’s Depth

April 2, 2021, 4:10-5:00pm | Zoom