Probability, Analysis, and Data Science Seminar, 2018-2019



Fall Semester


Wednesdays, 3:10pm, Carver 290



Title and Abstract


Organizational Meeting


Jennifer Newman (ISU)

Title: What’s in a Picture: Steganography and Digital Image Forensics

Abstract: This is an introductory talk on steganography, detection of steganography (steganalysis), and the CSAFE Project StegoAppDB.


Caleb Camrud, Evan Camrud, and Lee Przybylski (ISU)

Stability of the Kaczmarz Reconstruction for Stationary Sequences


Cathy O'Neil, Miller Lecture
"The Dark Side of Big Data"
9/11/18, 7:00pm, Great Hall, MU


Alex Neal-Riasanovsky (ISU)

Title: There's Probably Analysis and Data Science in a Graphon
Abstract: Graph limits (graphons) are an analytic version of the combinatorial object know as a graph. Born out of the Theory Group of Microsoft Research in Redmond, Washington in 2003 and motivated by long-standing trends in extremal combinatorics, data analytics, probability theory, and computer science, graphons have since spawned several new tools and unified old ones under a common theme. In this talk, we survey some recent results and applications.



Eric Weber (ISU)

Neural Networks and Ridgelet Transforms


Krishna Athreya (ISU)

Title: What is standard Brownian motion? Construction and some basic properties.



Tim McNicholl (ISU)


INFAS at UNL, 11/3/18


Joey Iverson (ISU)


Tom Needham (OSU)

Title: Gromov-Monge Metrics and Distance Distributions

Abstract: Applications in data analysis and computer vision often require a registration between objects; that is, a map from one object to another with minimal distortion of geometry. We give a flexible notion of object comparison which captures this idea by defining a metric on the space of all metric measure spaces (metric spaces endowed with probability measures). The metric, called Gromov-Monge distance, is defined by blending ideas from the theory of optimal transport with the Gromov-Hausdorff construction. We show that this distance has polynomial-time computable lower bounds defined in terms of classical invariants of metric measure spaces called distance distributions. Using tools from topological data analysis, we provide rigorous results on the effectiveness of these lower bounds when restricted to simple classes of mm-spaces such as metric graphs or plane curves.This is joint work with Facundo Mémoli.


Ananda Weerasinghe (ISU)

Optimal admission policies for matching queues


Ananda Weerasinghe (ISU)

Optimal admission policies for matching queues




Spring Semester




For more information contact:


Eric Weber; 454 Carver Hall; 294-8151; E-mail esweber at iastate dot edu


David Herzog; 474 Carver Hall; 294-6408; E-mail dherzog at iastate dot edu