About me

I am a middle school dropout that started college at 13-years-old to study math and computer science. I graduated from Stetson University in 2020 with a BS in mathematics and a minor in computer science. Along the way, I got involved in hackathons, math competitions, and even led some classes and workshops.

Now, I work with Python, SQL, and Tableau to dig into data, build financial models, and find insights that make an impact. Whether it’s pricing cryptocurrency options or forecasting financial trends, I enjoy applying math and programming to real-world challenges.

Outside of work, I spend time volunteering at an animal shelter, collecting Rubik’s Cubes, and making music.

Featured Project

Cryptocurrency Options Pricing: Black-Scholes vs Heston Model

This project explores the pricing of cryptocurrency options using two major financial models: the Black-Scholes Model and the Heston Stochastic Volatility Model. Traditional markets rely heavily on the Black-Scholes model, which assumes constant volatility. However, the extreme price swings of cryptocurrencies often make this assumption unrealistic. The Heston model accounts for stochastic volatility, potentially offering a more accurate valuation of crypto options.

Using real-world options data from Deribit, this project fetches historical option prices and computes theoretical values using both models. The visualization above compares these computed prices with actual market prices, helping to evaluate which model provides a better fit.

The results show that while Black-Scholes works well in stable market conditions, the Heston model better captures the dynamic volatility of crypto assets. This analysis is particularly useful for traders and researchers seeking to refine pricing strategies in highly volatile markets.

See my repository on GitHub!

My Math Blog

Recent posts...
Hopf Algebras in Topology and Quantum Groups 
Mathematics often resembles a sprawling bazaar, filled with structures and ideas that are surprisingly interconnected. Amid this mathematical marketplace, the Hopf algebra stands out as both enigmatic and indispensable. Combining the charm of algebraic structures with deep topological insight, Hopf algebras play a starring role in areas ranging from topology to quantum groups. In this post, we’ll explore how these algebras bridge the abstract and the physical, uniting loops, braids, and symmetries in a mathematical symphony that might just make you rethink what algebra can do...
Group Representation in High Energy Physics
High-energy physics, the field dedicated to unraveling the universe's smallest constituents, relies heavily on one surprising ally: symmetry. At its core, the mathematical study of symmetry is conducted using groups—structures that encapsulate transformations like rotations, reflections, and translations. But the plot thickens: in high-energy physics, these groups are not just abstract entities; they act on physical systems through representations. A group representation is essentially a way to make group elements tangible, allowing them to perform their mathematical gymnastics in the familiar arena of vector spaces. Let’s dive into the world of group representations, where symmetry reveals its role as both the universe's choreographer and a physicist’s favorite mathematical toy...
Path Integrals in Quantum Mechanics
If you’re accustomed to thinking of particles in physics as objects that move in a nice, neat line from Point A to Point B, brace yourself: quantum mechanics has other ideas. In the quantum world, a particle exploring the universe isn’t content with a single trajectory... it must, in some profound sense, explore every possible path all at once. Path integrals, formulated by the physicist Richard Feynman, are the mathematical framework that lets us account for this strange behavior. In this post, we’ll dig into the essentials of path integrals and see how they manage to capture the unruly motion of particles by considering every path a particle could take...

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