I'm particularly drawn to problems that sit at the intersection of mathematical theory and practical implementation. Whether it's applying graph theory to optimize network architectures, using statistical models to improve system performance, or finding elegant functional programming solutions to complex data transformations, I believe mathematical insights can lead to better software.
I grew up in Norway, the United States, and Poland and then studied Mathematics, Quantitative Science, and Linguistics at Emory University, and later pursued a Master's in Applicable Mathematics at the London School of Economics. Currently I am a Machine Learning Engineer at Comcast - TPX working on next generation communication and broadband networks through algorithm design, data science, and machine learning. This role builds on years of work spanning AI safety research, venture capital analysis, and even archaeological data - experiences that taught me the value of diverse perspectives in problem-solving.
When I'm not coding or writing, you'll find me biking around the city, making music, or picking up random hobbies and taking things apart. I enjoy exploring how older technological constraints influenced problem-solving approaches, and I love chatting through insights and ideas that my explorations lead to.