Winter 2019: Eileen Martin
Eileen Martin
Assistant Professor
Department of Mathematics, Computational Modeling and Data Analytics
PhD Computational and Mathematical Engineering, Standford University
MS Geophysics, Stanford University
BS Mathematics and Computational Physics, University of Texas-Austin
Email eileenrmartin@vt.edu
How do you see your work contributing to the goals and vision of IIHCC?
I definitely see myself being more on the computational side rather than the data acquisition side. I am interested in things like near-surface geophysics, asking questions such as what is the makeup of the top 100 meters of the Earth’s surface, how does it respond to seismic sources including earthquakes, and how well can it support buildings and other infrastructure? And I think in that sense I fit into the part of IIHCC investigating how the Earth interacts with the structures that we have built. Often, I am bringing in some of the more traditional geophysical methods but applied at a more human scale instead of the scale of the crust of the Earth. But, to bring things down to that scale, you have to have much larger higher-resolution data sets, and this is really difficult with existing software for many geophysical methods. Much of my work involves developing methods to process those data more quickly.
What other areas outside of your discipline would you entertain for future research and proposal work?
I am particularly interested in questions that are related to earth science but also topics in civil engineering and mechanical engineering, or questions dealing with how to create and manage energy. I have some experience in oil and gas, but many of that industry’s large-scale computing challenges are the same problems that are often faced by companies managing other renewable energy systems. Problems like that I think are really interesting. The methods I work on for seismic networks with many physical sensors may also carry over into monitoring and improving the efficiency of next-generation manufacturing facilities. The time-series data collected at these facilities typically have many spatial and temporal redundancies, much like seismic data around infrastructure.