What inspired you to a career in computational science?
My background is in applied mathematics. Through research experiences as an undergraduate, I found that I was drawn to problems in modeling the physical world. Computational models are increasingly being used to make high-impact decisions and to accelerate research and discovery across a wide range of fields. I was drawn to computational science because it enables me to apply my skills to some of the most important problems facing society today.
Describe the research your performed for your Ph.D. and how it improves society.
When a decision must be made about the future or in a situation for which data is unavailable, a computational model can provide key information to inform the decision. However, incomplete information about the phenomenon being represented and limitations in computational resources require approximations and simplifications that can lead to uncertainties in the model’s form and errors in predicted quantities of interest. My Ph.D. research focused on the development of an physics-informed uncertainty representation to account for approximations made in the development of a contaminant transport model. The overarching goal is to improve understanding of the reliability of computational models when they are used for prediction.
What’s the most challenging part of your work?
The beauty and the challenge of computational science is its interdisciplinary nature. You have to be careful to make the right engineering, mathematical, and algorithmic choices to ensure the accuracy of your work.
What are your plans after graduation?
I have accepted a position as a staff research scientist at Sandia National Laboratories in Albuquerque, NM.
Where do you see the field in ten years?
Uncertainty quantification (UQ) is computationally intensive, which has limited the scope of the studies that can be performed. In the next ten years, algorithmic advances and development of exascale computing capabilities will enable pioneering analyses of uncertainties in consequential areas such as modeling the effects of climate change.
What is a memorable experience at the Oden Institute?
In April of this year I participated in the Rising Stars in Computational & Data Sciences, an intensive workshop for women early in their research careers. It was hosted at the Oden Institute, which also helped to organize the event. I was deeply inspired to meet so many other young women in my field, as well as women with established careers in industry and academia.
Ph.D. Candidate CSEM ‘19
Supervising Professor: Robert Moser
M.A. (2016) – Computational Science, Engineering, and Mathematics; UT Austin
B.S. (2013) – Mathematics, The University of Alabama; Concentration: Math Numerical Track; Minor: Italian, Photography