Daniel O’Hara, Theresa Scarnati, Jeremy Yagle
Theresa Scarnati, Associate Research Mathematician, Air Force Research Laboratory
Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this talk, high order regularization techniques are developed for the application of synthetic aperture radar (SAR) image formation. A brief introduction to solving inverse problems using regularization methods will be given, followed by a quick overview of SAR. Then, we will discuss two research areas: (i) autonomous speckle reduction of SAR images using varaince-based joint sparsity, and (ii) joint SAR image formation and phase error correction. The presentation will conclude with information regarding the Autonomy Technology Research Center; a summer internship opportunity for undergraduate and graduate students that are American citizens.
Daniel O’Hara, PhD Candidate, University of Oregon
Landscapes evolve on timescales of thousands to millions of years through processes that uplift and erode surface topography, relative to the geoid. Often, these processes are posed in terms of plate tectonics or climate, which occur over large spatial (> 1010 m2 planform area) and temporal (> 105 yr) scales. Volcanic processes link tectonics to climate, and although volcanically generated topography occurs generally on smaller scales, the influence of volcanism on landscape evolution has yet to be explored in detail. Volcanic processes uplift the surface via shallow magmatic intrusions, edifice growth, and surface mantling by lava and ash deposition; and rapidly subside topography during explosive eruptions. Such processes often occur through short (day to decadal) events that may subsequently affect landscape form over millions of years. Furthermore, the large relief and persistence in time of volcanic landforms implies a nonlocal influence and disruption of larger areas than the initial structure. In this talk, I present recent work that explores volcanic effects on topography from a landscape evolution perspective. Using numerical modeling, I analyze landscape response to local disruptions in the form of volcanic perturbations and the expected signature of intrusive magmatism on surface topography. Finally, I discuss preliminary work to determine volumes of recent (< 2x106 yr) extrusive volcanism within the Cascades arc of the Western US.
Jeremy Yagle, Technical Lead for LaRC’s Data Science Team within the Office of the Chief Information Officer, NASA
In 2014, a team of researchers and information technology specialists at NASA Langley Research Center (LaRC) developed a data science strategy as part of LaRC's Comprehensive Digital Transformation Initiative, with the goal of developing long-term capabilities for data analytics and machine learning in aerospace domains. Over the past four years, significant progress has been made in developing pilots and projects through a strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This talk will present a summary of the technical aspects of the work, as well as the progress made in collaboration, outreach, and education.Sept. 12, Alumni Day
Originally from Erie, PA, Dr. Theresa Scarnati received a BS in applied mathematics from Indiana University of Pennsylvania in 2014. After completing her MA in 2016, she graduated with her PhD in applied mathematics from Arizona State University in 2018. Currently, Dr. Scarnati is an associate research mathematician for the Air Force Research Laboratory within the Mutli-Domain Sensing Autonomy division. Her research interests include the implementation and analysis of regularization techniques for exploiting sparsity and prior knowledge in inverse problems, specifically for the application of denoising synthetic aperture radar (SAR) images, SAR automatic target recognition, three-dimensional image reconstruction and multi-sensor information fusion.
“I am a fifth-year PhD student in the Department of Earth Sciences at the University of Oregon (UO), studying volcanic geomorphology. I am a first-generation student, originally from Ebensburg, Pa. I attended IUP from 2009 to 2014, where I completed a dual degree in geology and computer science, with a minor in mathematics. As an undergraduate, I was a S-COAM scholar, McNair scholar, and Goldwater Scholar, which allowed me to participate in multiple research projects in the IUP Geoscience Department, at the Virginia Institute of Marine Science (VIMS), and at Academia Sinica (Taiwan). Motivated by my experiences at IUP, I applied and was accepted to UO in 2014. At UO, I have focused my research into understanding the interaction between volcanic processes and surface topography. As a graduate student, I have co-authored two research articles, recently submitted my first primary-author paper for review, interned at the Cascades Volcanic Observatory through the NSF Graduate Research Internship Program (GRIP), and was awarded an NSF Graduate Research Fellowship (GRF).”
The technical lead for LaRC’s Data Science Team within the Office of the Chief Information Officer. Before joining the team at Langley, he worked at NASA’s Independent Verification and Validation (IV&V) Facility to develop algorithms for the strategic assessment of new technologies related to NASA mission goals, with the US Army Corps of Engineers (Pittsburgh District) in the Navigation Design Branch of the Engineering and Construction Division, spent nearly 10 years as a Child Protective Services caseworker in Pennsylvania, and served in the US Navy. Jeremy holds a Master of Science degree in Applied Mathematics from Indiana University of Pennsylvania, a Bachelor of Arts degree in Religious Studies from Pennsylvania State University, and is a graduate of Naval Nuclear Power School.