Research

My research lies at the intersection of biology, mathematics, and computation. I first explored how mathematical models can reveal patterns in complex systems, and then expanded this work through a mentored project with a professor at William & Mary. We applied Takens’ Delay Embedding and Gaussian Process Regression to study chaotic ecological dynamics, testing how mathematics can be used to reconstruct and forecast real-world systems.

Looking ahead, I want to build a strong foundation in biology at the undergraduate level and continue advancing into interdisciplinary fields such as computational biology and bioinformatics. My goal is to connect theory with application by using data, mathematics, and computation to better understand the living systems that sustain life.

Mentored Research

Reconstructing Chaotic Dynamics with Delay Embedding and Gaussian Process Regression and Applying the framework to ecological biomass data

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Independent Research

Exploring Genetic Disparities and Predictive Modeling in Gastric Cancer: A Statistical and Machine Learning Approach Using TCGA Data

 

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