About Me
Passionate about leveraging technology to solve complex healthcare challenges
I'm a student at UC Berkeley pursuing a unique combination of Computer Science, Electrical Engineering, and Bioengineering. Growing up in Pembroke Pines, Florida, I developed passions for technology, basketball, sports, movies, and the NYT Crossword. The discipline and teamwork I learned on the court have shaped my approach to solving complex problems in healthcare and technology.
Currently, I'm working on groundbreaking projects at Memorial Sloan Kettering Cancer Center, where I'm developing predictive and probabilistic models towards metastatic chronology, hoping to provide treatment to tumors ahead of its spread. My goal is to leverage machine learning and data engineering to make healthcare more accessible and effective for everyone. When I'm not coding or researching, you'll find me on the basketball court or collaborating with fellow students on innovative healthcare solutions.
Professional Experience
Building the future of healthcare through AI and data science
Chicago Trading Company
Upcoming internship in quantitative development and algorithmic trading strategies.
Nvidia
Designed real-time market intelligence dashboard, built backend API with VC scoring and data pipelines, created structured taxonomy of 30+ strategic investors.
Memorial Sloan Kettering Cancer Center
Developed DAG-based models to predict metastatic chronology and built ancestry-aware cancer risk classifiers.
T-Mobile
Developed intelligent data discovery pipeline and deployed REST API on Databricks' Unity Catalog.
Quest Diagnostics
Built experiment logging infrastructure and created time-series classification model for lab test volume forecasting.
Sky Computing Lab
Led optimization of LEANN, reduced storage by 97%. Enhanced system scalability, lowered build-phase memory by 40%, boosted query throughput by 25%. Integrated IDE-embedded semantic search via Claude Code MCP.
Berkeley Artificial Intelligence Research (BAIR)
Led benchmarking of non-coding variant effect models, improved prediction consistency by 35%. Developed in-silico mutagenesis and motif-ablation tools. Co-designed Scooby-based single-cell modeling pipeline.
UCSF Tech Lab
Led development of multi-output Random Forest for non-invasive glucose/HbA1c prediction, achieved MAE = 9.8 mg/dL (9% error). Implemented SHAP-based fairness audits, reduced group-wise error disparity by 18%.
National Institutes of Health (NIH)
Led equity-focused AI research, improved cross-demographic model fairness by 22%. Developed fairness-constrained deep models, reduced racial bias by 30%. Authored manuscript "Improving Equity in AI Through Responsible Data Sharing."
Scripps Research - Andersen Lab
Led optimization of outbreak.info Python API, increased response density from 11% to 98%. Enhanced SARS-CoV-2 lineage tracking capabilities.
National Science Foundation (NSF)
Led development of 1D-CNN for breast-cancer subtype prediction, achieved 95% accuracy. Published results on CNN-based diagnostic acceleration, demonstrated 6x faster inference.
Featured Projects
Innovative solutions combining AI, healthcare, and social impact
Hunger Hotspot Predictor
Architected full-stack ML pipeline and deployed React dashboard. Technologies: Python, PyTorch, TensorFlow, FastAPI, PostgreSQL.
View on GitHubPredicting Trends in Research Papers
Built NLP preprocessing and classification pipeline, containerized entire pipeline with Docker. Technologies: Python, Docker, Git, Scikit-learn, AWS.
View on GitHubCancer Subtype Classification
1D-CNN for breast-cancer subtype prediction with 95% accuracy. Published results on CNN-based diagnostic acceleration, demonstrated 6x faster inference.
View on GitHubNIH All of Us Research Program
Led equity-focused AI research, improved cross-demographic model fairness by 22%. Authored manuscript "Improving Equity in AI Through Responsible Data Sharing."
View on GitHubLEANN Optimization
Led optimization of LEANN system, reduced storage by 97%, lowered build-phase memory by 40%, boosted query throughput by 25%. Integrated IDE-embedded semantic search.
View DetailsNon-coding Variant Effect Models
Led benchmarking of non-coding variant effect models, improved prediction consistency by 35%. Developed in-silico mutagenesis and motif-ablation tools.
View DetailsMulti-output Random Forest for Glucose Prediction
Led development of multi-output Random Forest for non-invasive glucose/HbA1c prediction, achieved MAE = 9.8 mg/dL (9% error). Implemented SHAP-based fairness audits.
View DetailsPublications
Contributing to scientific knowledge through research
NSF Research: Machine Learning Approaches to Cancer Classification
Novel machine learning approaches for cancer classification and prediction using genomic data.
Read PaperShort Bowel Syndrome: A Case Study of Multiple Micronutrient Deficiencies
Comprehensive analysis of micronutrient deficiencies in short bowel syndrome patients.
Read PaperAwards & Recognition
Recognized for excellence in academics, leadership, and service
2024 Coca-Cola Scholar
Selected as one of 150 high school seniors nationwide from a pool of over 100,000 applicants for academic excellence, leadership, and service.
2025 Stephen J. Brady Stop Hunger Scholar
Recognized for developing AI-based predictive modeling tools to optimize food distribution and serving over 100,000 people through Joshua's Heart Foundation.
2024 10 Under 20 Food Hero
Selected for innovative contributions to food security and sustainable food systems.
Silver Knight Award in Mathematics
Honored for exceptional academic achievement and community service in computer science and innovation.
Points of Light Award
Recognized for outstanding community service and leadership in fighting food insecurity across Florida, India, and globally.
Nonprofit Leadership
Making a difference through technology and service
Joshua's Heart Foundation
Led operations and developed AI-based predictive modeling tools to optimize food distribution, directly impacting over 100,000 people in need. Recognized as a 2025 Sodexo Stop Hunger Scholar for innovative approaches to fighting food insecurity.
Bread for the World
Implemented data-driven solutions to drive hunger relief and policy change, focusing on sustainable food security solutions and advocacy for systemic change.
Skills & Expertise
Technical skills across multiple domains