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 of 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 probabilistc 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.
Developing predictive analytics for metastatic chronology/sequencing and engineering genomic data processing pipelines.
Building intelligent data discovery systems and enhancing search capabilities for enterprise users.
Fairness and explainability in multimodal machine learning models within digital health.
Working on startups and helping to assist the growth of them. Portfolio is around $800m+ in investments. See readysethealth.io for more information.
Conducted time-series forecasting models for lab test volumes and helped to improve the accuracy of the models for the financial team. Improved predictive analytics and supply chain forecasting at a 97% accuracy rate.
Examined change in GI research due to the use of AI and COVID-19.
Part of the Andersen Laboratory working on metagenomic sequencing of COVID-19 clinical samples and building sequencing data analysis pipeline.
Improved prediction models for AI utilizng GANs use in medicine, focusing on equity and accessibility in healthcare technology. Published comprehensive research on AI implementation in healthcare settings.
Led research on machine learning approaches to cancer classification, developing novel algorithms for genomic data analysis. Published findings in the Journal of Emerging Investigators.
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.
Implemented data-driven solutions to drive hunger relief and policy change, focusing on sustainable food security solutions and advocacy for systemic change.
Selected as one of 150 high school seniors nationwide from a pool of over 100,000 applicants for academic excellence, leadership, and service.
Recognized for developing AI-based predictive modeling tools to optimize food distribution and serving over 100,000 people through Joshua's Heart Foundation.
Selected for innovative contributions to food security and sustainable food systems.
Honored for exceptional academic achievement and community service in computer science and innovation.
Recognized for outstanding community service and leadership in fighting food insecurity across Florida, India, and globally.
Novel machine learning approaches for cancer classification and prediction using genomic data.
Read Paper →Comprehensive analysis of micronutrient deficiencies in short bowel syndrome patients.
Read Paper →ML system identifying food insecurity patterns across the US, improving supply chain efficiency by 89%.
View on GitHub →Deep learning system for cancer subtype prediction with 95% accuracy using 1D-CNN architecture.
View on GitHub →Analysis system for tracking and predicting medical research publication trends.
View on GitHub →Led ML development for classification & segmentation of karyotypes for aneuploidies as Founding Engineer. (Jan 2025 - Mar 2025)
View Details →Built a financial tracking model for consumer use as Founding Full-Stack Developer. (Jan 2025 - Mar 2025)
View Details →Research Scholar improving prediction models for AI use in medicine. Published comprehensive research project. (Sep 2023 - May 2024)
View on Github →Led discussions and research on investing, drug development, health economics, and public policy. (Sep 2024 - Dec 2024)
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