About Me

Biodesign AI Fellow at UCLA Health | Data Science in Biomedicine Masters of Science | University of California, Los Angeles (UCLA)

I am a graduate student at the University of California, Los Angeles (UCLA) studying Data Science in Biomedicine, and I am passionate about using data science to solve challenges in healthcare and biomedical industries. With expertise in machine learning, predictive analytics, and data visualization, I transform data into actionable insights to improve healthcare systems and outcomes.

Currently, I am a Biodesign AI Fellow at UCLA Health, collaborating with experts to refine my skills in real-world applications of data science. My experience includes a summer internship in data science at the Children's Hospital of Orange County, where I worked on impactful projects in a fast-paced environment. I also hold a Bachelor of Science in Data Theory at UCLA, which provided me with the technical expertise and analytical foundation for my graduate studies.

I aim to bridge healthcare data and decision-making, creating solutions that enhance efficiency, reduce costs, and improve care.

Browse the rest of my website to see more details about my experience and projects. Let’s connect to explore how data science can make a meaningful impact in healthcare!

Interests

  • Data Science, Mathematics, Statistics
  • Biomedicine, Healthcare, Technology
  • Machine Learning, Deep Learning, Artificial Intelligence

Education

  • Data Science in Biomedicine M.S. | University of California, Los Angeles (UCLA)
  • Data Theory B.S. | University of California, Los Angeles (UCLA)

Experience

UCLA Biodesign

Biodesign AI Fellow

Sep 2024 - Present | Los Angeles, CA

  • Leveraged SQL to process, clean, and integrate OB/GYN patient flow data from a dataset of 80,956 rows spanning 2022–2024; conducted exploratory analysis using visualizations to uncover trends in bed usage, delivery patterns, and departmental efficiency metrics.
  • Designed, implemented, and evaluated an LSTM model to forecast weekly OB capacity trends, training on 2022–2023 data; achieved a test RMSE of 17.69, providing actionable insights to optimize resource and staff allocation, as well as enhance operational planning.
  • Partnered with a team of six to analyze patient flow data within a school’s comprehensive data repository; developed a project strategy, presented findings, and proposed actionable next steps, emphasizing opportunities to improve efficiency and data-driven decisions

Children’s Hospital of Orange County (CHOC)

Data Science Intern

Jun 2023 - Aug 2023 | Orange, CA

  • Analyzed demographic disparities among diagnosed anxiety patients by constructing multiple linear regression models using Python’s Statsmodels and SciPy libraries, achieving 77% accuracy; conducted t-tests and other statistical analyses to identify actionable insights.
  • Led a team of four data science interns in delivering data-driven insights to physicians through visually engaging presentations; created barplots, box plots, model summary tables, and geographical heat maps to highlight trends and promote predictive measures in mental health
  • Developed and proposed detailed action plans for deploying advanced unsupervised machine learning techniques to uncover latent factors contributing to anxiety disorders in pediatric patients, supporting data-driven interventions and improved outcomes.

Projects

To see each project in greater depth, click on their icons!

  • Ronald Reagan Medical Center
    Click to See GitHub Page!

    Bed Capacity Data Analysis for Ronald Reagan Medical Center from Q1 2022 to Q3 2024

    A data analysis and time series forecasting of bed capacity in the delivery ward of the OB/GYN department at Ronald Reagan Medical Center, covering the period from early 2022 to September 2024, aimed at assisting UCLA Health in enhancing patient flow and operational efficiency

  • CHOC
    Click to See GitHub Page!

    Analysis of Anxiety Trends from CDC Data: Q2 2020 to Q2 2023

    An analysis of demographic disparities among individuals showing indications of anxiety, based on CDC data from April 4th, 2020 to June 30th, 2023, aimed at providing CHOC medical staff and physicians with insights into potential contributing factors to anxiety

  • Fingerhut
    Click to See GitHub Page!

    Data Analysis of Fingerhut Website Customer Traffic Behavior from Q4 2020 to Q3 2023

    An analysis of website data from the online retailer Fingerhut, covering the period from November 2020 to September 2023, conducted to provide feedback on optimizing customer navigation and aligning with the company's business needs

  • DataFest 2022
    Click to See GitHub Page!

    Analyzing Player Data from the Yale School of Medicine Play2Prevent Educational Video Game (DataFest 2022)

    Analyzed data from the Yale School of Medicine's Play2Prevent educational video game "Elm City Stories", focusing on player behavior within a middle school cohort, to offer insights for enhancing game design and optimizing educational outcomes

Resume