Experience & Experiences
I have had many opportunities that have let me explore my path and determine what I want to do. From fieldwork on the Yucatán Peninsula to research on Machine Learning for Enhanced Oil Recovery, I’ve capitalized on many opportunities and gained life-defining experience.
Below, you’ll find three of my highlights: my work at INTERA Inc., an ML research internship at the University of Texas, and the research experience that sparked my interest in this field.
Under the highlights is a summary of my other experiences that have led me to where I am today. If you have any questions, please reach out and ask!
INTERA’s Austin office 5k in March 2025.
Groundwater Modeler | INTERA Inc.
My responsibilities revolved around supporting the development of numerical models for groundwater flow at the Hanford Site. I created and managed model inputs, developed and refined Python- and R-based post-processing workflows, and integrated large monitoring datasets. I also performed geospatial analysis in ArcGIS Pro and Python to prepare model boundaries, grids, and well datasets. In addition, I generate and analyze outputs from MODFLOW and MODPATH, including residual plots, concentration–pumping overlays, and time-series visualizations, ensuring quality control through automated filtering and validation. My work supports the team in compiling and interpreting data for calibration, scenario analysis, and regulatory deliverables, as well as in preparing technical reports and presentations for clients and stakeholders.
Machine Learning Research | Hildebrand Department of Petroleum and Geosystems Engineering
The Research
This project was supervised by Dr. Maša Prodanović and Dr. Bernard Chang to optimize an existing machine-learning model that characterizes 3D rock samples by estimating permeability. Physics-based models are available to determine permeability; however, they are computationally expensive and time-consuming. MS-Net bridges this gap by estimating permeability based on previously simulated data. My research aimed to incorporate the Fast Fourier Transform to increase the accuracy of MS-Net and reduce errors.
Results
Incorporating FFT into the existing MS-Net model improved performance by enhancing the accuracy and restoration of the digital image slices.
Enhancing the accuracy of this machine learning model enables a more accurate representation of the subsurface, which can inform decision-making in various industries and fields by increasing accuracy and reducing time and computational costs. My efforts during this research culminated in the poster “Incorporating the Fast Fourier Transform into a Deep Learning Workflow” (seen above).
Groundwater Research | Jackson School of Geosciences
Background
This project, supervised by Dr. Bayani Cardenas and Dr. Daniella Rempe, aimed to characterize the hydrogeology of a barrier island in Mexico off the coast of the Yucatán Peninsula. Barrier islands are hydrologically unique due to saltwater on one side and brackish water on the other; however, this, paired with tidal influences, increases the vulnerability of their groundwater resources.
Results
We determined that this island’s groundwater is highly vulnerable to overpumping and is significantly influenced by tidal effects. This wasn’t necessarily a shocking conclusion (after all, the island is only about 50m long, or about half a football field for any Americans); however, it was exciting and rewarding to see a direct application of hydrogeology and to see how it affects people’s daily lives. I presented this research at the 2023 American Geophysical Union meeting in San Francisco as a poster titled “Physical and Chemical Hydrogeology of a Barrier Island in the Yucatan Peninsula”.
Additional Experience & Experiences
Outside of the highlighted experience above, I have had plenty of other Experiences that have helped define my career thus far. Below are two maps showing these Experiences with short blurbs describing them. For the sake of legibility, I split them into maps. One focuses on the different avenues I explored during my undergraduate education at the University of Texas, and the other shows a more global distribution of my experiences across the US and abroad.
On Campus at the University of Texas at Austin
Broader Experiences
Presentations & Awards
Presentations
SURI Research Poster Session: “Incorporating the Fast Fourier Transform into a Deep Learning Workflow”
Longhorn Research Poster Session: “Physical Hydrogeology of a Barrier Island in the Yucatan Peninsula”
The Jackson School of Geosciences Symposium: “Physical Hydrogeology of a Barrier Island in the Yucatan Peninsula”
American Geophysical Union 2023, San Francisco: “Physical and Chemical Hydrogeology of a Barrier Island in the Yucatan Peninsula”
Awards
Groundwater Field Methods Award (2023-24)
August, 2024. Presenting my poster “Incorporating the Fast Fourier Transform into a Deep Learning Workflow”