At NASA Langley Research Center, I am involved in the Comprehensive Digital Transformation, which is an initiative focused on developing and implementing innovative data analytics and machine intelligence solutions for complex problems in Langley’s
aerospace domain. Our work is centered on two key areas:
Data Intensive Scientific Discovery: Automated mining of images, experimental data, and computational data that will help NASA scientists derive new insights and make new discoveries. The tools we develop in this area incorporate the underlying
physics into the machine learning algorithms.
Deep Content Analytics: Knowledge mining of scientific literature, web content, and multimedia sources that will allow NASA researchers to quickly analyze vast collections of information in order to answer specific questions. The tools
in this area rely on natural language processing and IBM Watson technologies.
The overall goal of our work is to provide NASA scientists with new tools that will lead to greater scientific discoveries and system design optimizations. While I am involved with several different project across the two key areas, the primary
focus of my research is on the detection and prediction of aeroelastic flutter from wind tunnel test data.
Feb.23, 2016Dr. Fern Hunt, Research Mathematician, Mathematical Modeling Group, National Institute of Standards and Technology
11:00-11:50 a.m. Pratt Auditorium
Title: A Mathematical Look at Paint, Hollywood, and Networks
This talk will present several examples of the kinds of problems a mathematician can encounter at NIST. They illustrate the breadth of applications mathematical ideas.
3:30-4:30 p.m. STRGT 226/22
Title: An Algorithm for Identifying Optimal Spreaders in a Random Walk Model of Network Communication
In a model of network communication based on a random walk in an undirected graph, what subset of nodes (of some fixed size), enable the fastest spread of information? The dynamics of spread is described by a process
dual to the movement from informed to uninformed nodes. In this setting, an optimal set A minimizes the sum of the expected first hitting times (F(A)), of random walks that start at nodes outside the set. Identifying such
a set is a problem in combinatorial optimization that is probably NP hard. Fortunately, F has been shown to be a supermodular and non-increasing set function.
In this talk, the problem is reformulated so that the search for solutions to optimization problem is restricted to a class of optimal and "near" optimal subsets of the graph. We will discuss our approach to the approximation and solution of this problem based on properties of the underlying graph.