Research Intern
University of Texas at Austin
June 2024 - August 2024
Worked on developing a machine learning algorithm to classify methane super emitters from satellite data
Skills
- HTML
- CSS
- Assembly
- Python
- C
- Java
- Matlab
- OCaml
- Git
Education
University of Maryland | B.S. in Computer Engineering
Expected Graduation: May 2026
About Me
I am a junior at the University of Maryland studying Computer Engineering. I am interested in both software and hardware development. I recently had the oportunity of holding a research internship at the University of Texas at Austin and will be continuing my research. I plan to present my work at the AGU (American Geophysical Union) 2024 conference.
Experience
Software Developer Intern | MPECWatch
UMD Small Bodies Node
August 2022 - Present
Developed the MPECWatch website using Sqlite3 and Bootstrap
ASPIRE Intern
Johns Hopkins Applied Physics Lab
September 2021 - May 2022
Developed software using OpenCV to enable cheap cameras to detect microplastics ∙ Developed a software model that would simulate optimal spacecraft landing areas using OpenCV
Projects
Customizable Asynchronous Tools for UNIV100
I recently had the opportunity to work on an impactful project aimed at streamlining course delivery and support for UNIV100. Our team successfully designed and implemented a comprehensive asynchronous training program for instructors and coordinators, which included flexible syllabus customization and ongoing support. By facilitating virtual check-ins and offering a Question Line through Google Surveys, we ensured that all participants had access to the resources they needed for effective course implementation. This project not only improved consistency and clarity in course delivery but also fostered collaboration and adaptability among team members. It was rewarding to see the positive outcomes and to contribute to enhancing the learning experience for students.
MPECWatch
MPEC Watch is a web application that provides astronomers with valuable insights into minor planet data. Using Python, SQL, and Bootstrap, I developed a tool that queries a large SQL database and generates informative visualizations. The project focuses on individual station observatory browsers, where I'm responsible for creating user-friendly interfaces using Bootstrap. I've overcome challenges related to data cleaning and visualization, implementing efficient SQL queries and leveraging Matplotlib for data plotting. In the future, I plan to expand MPEC Watch to include additional features such as real-time data updates and more advanced analysis tools.
Arduino Remote Heartbeat BPM Simulator
As the leader of a five-member hackathon team, I spearheaded the development of an Arduino-based heartbeat simulator. This interactive educational tool uses LEDs within a 3D-printed heart model to visually represent a beating heart. A remote control allows users to adjust the heart rate via an IR receiver, providing a tangible and engaging experience while providing descriptors of the heart rate based on heigh, weight, and age. The project required extensive collaboration and communication among team members, as well as proficiency in Arduino programming and circuit design. I'm proud of the final product and the positive feedback we received from our peers.
EMIT Image Labelling Application
During my research at the University of Texas at Austin, I developed an efficient image labeling tool to streamline the
identification of methane super-emitters from satellite imagery. This tool leverages the Google Maps API to query nearby
locations based on satellite image coordinates (longitude/latitude), enabling intuitive labeling of unmarked emitters. A
key feature is its ability to batch images by geoproximity, allowing users to label clusters of emitters in nearby
areas, significantly speeding up the process. Labeled images are stored in a custom-designed database for easy access,
analysis, and future use.
In addition to increasing efficiency, the tool supports scalability for larger datasets, making it adaptable for broader
environmental monitoring and research applications. My work has been recognized by the scientific community, and I will
present my findings at the American Geophysical Union (AGU) 2024 conference in Washington, D.C. this December. This tool
has potential for wide application in tracking methane emissions, contributing to efforts to combat climate change.