About
I am a graduate of the University of Texas at Austin with a Bachelor of Science in Mathematics, a minor in Business, and a certificate in Scientific Computation & Data Sciences. I am a current Data Science Graduate Student at Boston University.
I conducted research at UT’s Urban Ecosystem Streams, focusing on environmental science and data analysis. My responsibilities encompassed exploratory data analysis, statistical analysis and modeling, data visualizations, data science, and data mining.
In my free time, I enjoy swimming, running, traveling, mahjong, photography, and playing the viola and the piano.
Skills
Python
R
SQL
HTML
CSS
JavaScript
Java
Excel
Tableau
Power BI
Azure
MATLAB
C++
Snowflake
Amazon Web Services (AWS)
React
Experience
University of Texas Urban Ecosystems Stream
Postbaccalaureate Researcher
August 2024 - July 2025
- Conducted an individual research project on the impact of organic pollutants on human health in Pacific Northwest streams to assess potential health risks to humans.
- Developed machine learning models to predict Waller Creek pollutant levels which have resulted in a 22% increase in accuracy compared to previous models.
- Mentored 108 students in Biology and Chemistry Labs and provided guidance in data analysis and research methods.
Prime Process Safety Center
Process Safety Lab Technician (part-time)
August 2024 - March 2025
- Streamlined workflows to automate data processing that resulted in faster analysis in chemical data by 28%.
- Documented and analyzed test results from experimental data that improved safety protocols by identifying key safety metrics.
- Maintained and calibrated laboratory equipment which reduced errors and enhanced operational efficiency in data collection.
Freshman Research Initiative - Urban Ecosystems Stream, UT Austin
Undergraduate Researcher
August 2020 - December 2021
- Utilized Excel to analyze large datasets that improved the accuracy of environmental trends forecasting through predictive modeling.
- Collaborated with researchers to analyze and transform environmental data into recommendations that enhanced Austin’s water quality.
- Conducted laboratory analyses that ensured data accuracy and improved the reliability of environmental modeling for future research initiatives.
Projects
Multimodal Data for Predictive Failure and Anomaly Detection
February 2026 - Present
- Building an end-to-end pipeline to detect anomalies and predict failures in large-scale systems using time-series, sensor, and log data.
Cancer Mortality & Prevention
January 2026 - March 2026
- Developing a random forest model to identify key causes of cancer mortality in the United States.
- Integrating CDC data and validating results with cross-validation and analyses to support cancer prevention.
Spotify Top Songs Over 9 Years
December 2025 - March 2026
- Analyzing Top Songs on Personal Spotify from 2017 to 2025 by utilizing support vector model to improve model selection on datasets.
- Refining a decision tree model to enhance predictive accuracy on top songs datasets.
Portfolio Website
April 2025 - May 2025
- Developed a portfolio website using HTML and CSS for front-end development.
- Utilized JavaScript to develop both the front-end and back-end of the portfolio website.
Portfolio Website
April 2025 - May 2025
- Developed a portfolio website using HTML and CSS for front-end development.
- Utilized JavaScript to develop both the front-end and back-end of the portfolio website.
The Impacts of Organic Pollutants on Humans in the Pacific Northwest Streams
January 2025 - April 2025
- Applied machine learning and regression to analyze pollutant distribution and identify health risk trends.
- Performed data cleaning on RSQA data for reliable analysis.
- Assessed the bioactivity of 14 pollutants using the ToxCast program to identify biological targets and evaluate health impacts.
Investigating the Impact of Chemical Pollutants on Streams and Health using Exploratory Data Analysis
January 2025 - April 2025
- Developed a machine learning algorithm that improved environmental risk assessments on pollutant concentrations during baseflow and runoff conditions.
- Built interactive visualizations that identified correlations between organic pollutant levels and environmental factors which enabled mitigation strategies on 485 streams.
- Presented at the 2025 University of Texas at Austin’s College of Natural Science’s Technology & Science Undergraduate Research Forum.
Optimizing U.S. Air Pollution Prediction with Machine Learning and Cost-Effective Monitoring
April 2024 - May 2024
- Developed machine learning models that analyze air quality data and improved forecasting accuracy through model optimization.
- Designed cost-effective monitoring systems that reduce operational costs for predicting air quality.
Comparative Analysis of Non-Linear SVMs and Decision Trees on Simulated and Real-World Data
April 2024 - May 2024
- Utilized machine learning to build financial models that improved model selection and cost-saving measures.
- Refined model selection that enhanced predictive accuracy for financial risk assessment and cost-benefit analysis.
Urban Ecosystems: Habitat Assessment
January 2021 - April 2021
- Developed a comprehensive assessment of urban ecosystems in Austin by analyzing Fecal Indicator Bacteria (FIB) levels and their correlation with urban infrastructure.
- Quantitative and qualitative analyses were conducted across urban and green spaces using YSI multiprobe, CHEMetrics Kits, and PAM air quality monitors to evaluate water quality.
- Molecular Source Tracking was applied to identify human fecal contamination hotspots, and the impacts of greenspace on air and water quality were assessed to enhance the understanding of urban environmental health impact sustainable urban planning and policy decisions.