Leveraging my passion for programming, innovation, and scientific discovery, I tackle exciting challenges in computational biology and bioinformatics. My goal is to develop open-source machine learning, statistical, and computational software that empowers researchers to uncover meaningful insights from complex biomedical data, ultimately contributing to a deeper understanding of human health and disease. I'm committed to creating user-friendly tools with strong data visualization principles and plan to pursue a PhD to advance novel methodologies for the broader scientific community.
A showcase of my work in machine learning, deep learning, computer vision, computational biology, full-stack development, and software development
A pipeline built in R and Python to convert spatial transcriptomics data into pixel-based images for visualizing cell segmentations and RNA species distributions. Utilizes ResNet models, dimensionality reduction techniques (PCA, t-SNE), and clustering algorithms to identify subcellular RNA heterogeneity.
An R package designed for visualizing proportional data across spatial coordinates through scattered stacked bar plots. Built with ggplot2, dplyr, and tidyr, offering customizable visualizations for spatial data analysis and published on GitHub.
A machine learning model built using PyTorch to predict COVID-19 hospitalization and ICU admissions based on CDC surveillance data. Achieved a 94.4% PR AUC score with a Random Forest classifier through hyperparameter optimization and 3-fold cross-validation.
A real-time drowsiness detection system using OpenCV, Mediapipe, and dlib for facial landmark recognition. Analyzes Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to classify driver states, achieving 70.23% accuracy.
A full-stack chatbot developed to provide accessible cancer education to patients and families. Built with HTML, CSS, JavaScript, and React, featuring dynamic chatboxes, response delay handling, and accessibility improvements.
A collaboration with Universidad de Madrid exploring the Lottery Ticket Hypothesis through genetic algorithm development for optimizing supermasks in convolutional neural networks, achieving 8.5% performance improvements over random guessing.
Research contributions in computational biology and bioinformatics
Programming languages, packages, toolkits, software, and systems I work with and am proficient in
Download my full resume for detailed information about my experience and qualifications
Software Developer & Post-bacc Student
PDF • Updated May 2025B.S. Computer Science & Chemical Engineering
Johns Hopkins University
Software Developer
JEFWorks Lab
Computational Biology
Spatial Transcriptomics
Computer Vision
Deep Learning
I'm always interested in discussing new opportunities and collaborations! (◕ っ ◕✿)
