I am a Ph.D. Candidate in Statistics at the University of Wisconsin-Madison, where my research focuses on developing robust, geometric and topological methods for nonlinear high-dimensional data spaces. My work involves creating efficient tools for reconstructing and representing complex, high-dimensional structures, with a particular focus on handling irregular time series and nonlinear systems.

I've also explored the intersection of machine learning and topology, extending traditional ML techniques to analyze and understand topological features. Notably, I've introduced the cycle community method for identifying and leveraging 1-dimensional homology generators, such as loops, within complex networks. Additionally, I developed a spectral algorithm for efficiently extracting cycles and loops in large, dense networks, enhancing the representation of high-dimensional data.

Outside of Statistics, I enjoy soccer and reading. I am also quite passionate about the interplay between physiology and psychology.

• Beneath a Scarlet Sky - Mark T. Sullivan
• The Art of Invisibility - Kevin Mitnick
• Deep Work - Cal Newport
• Quiet: Susan Cain
• Between the World and Me - Ta-Nehisi Coates
• If I Stay - Gayle Forman
• Zoe Bentley Mystery Series (4 Books) - Mike Omer
• The Little Book of Common Sense Investing - John C. Bogle
• Escaping Gravity - Lori Garver

• Why Nations Fail: The Origins of Power, Prosperity, and Poverty - Acemoglu and Robinson