
Fall Semester


Wednesdays, 3:10pm, Carver 290


Speaker

Title and Abstract

8/22/18


Organizational Meeting

8/29/18

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.

9/5/18

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

Stability of the Kaczmarz Reconstruction for Stationary Sequences

9/12/18


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

9/19/18

Alex NealRiasanovsky (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 longstanding 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.

9/26/18



10/3/18

Eric Weber (ISU)

Neural Networks and Ridgelet Transforms

10/10/18

Krishna Athreya (ISU)

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

10/17/18



10/24/18

Tim McNicholl (ISU)


10/31/18


INFAS at UNL, 11/3/18

11/7/18

Joey Iverson (ISU)


11/14/18

Tom Needham (OSU)

Title: GromovMonge 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 GromovMonge distance, is defined by blending ideas from the theory of optimal transport with the GromovHausdorff construction. We show that this distance has polynomialtime 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 mmspaces such as metric graphs or plane curves.This is joint work with Facundo Mémoli.

11/28/18

Ananda Weerasinghe (ISU)


12/5/18






Spring Semester



For more information contact:
Eric Weber; 454 Carver Hall; 2948151;
Email esweber at iastate dot edu
David Herzog; 474 Carver Hall; 2946408;
Email dherzog at iastate dot edu

