Growing up in South Bend, Indiana, I always enjoyed math. I initially spent more time on hockey and weightlifting, but once I realized that I had more talent in math, I leaned into it hard. In high school I self-studied MIT's undergraduate math/physics curriculum, worked on a solo math project (constructing and proving a closed-form expression for decomposing a general rational expression into partial fractions), and got involved in physics research labs at IUSB and Notre Dame, where I completed two projects that improved data transmission in particle detectors. I was hooked on the goal of discovering all fundamental truths about the universe, and although physics research was a positive experience, the slow pace made me feel like I wasn't making enough progress toward that goal. I figured it was worth exploring an indirect approach: discover the neural basis of intelligence and use it to create thinking machines or make scientists more intelligent.


In 2014, I attended Notre Dame on a full-ride Lilly Scholarship and majored in pure math. I was entirely consumed by a quest to understand the human brain from first principles by modeling biological neural networks, which meant that all the time one normally uses for studying and attending class was in my case spent working on independent projects. That summer, I interned with the synthetic cognition group at Los Alamos National Lab, attempting to create an emergent phenomenon of brain oscillations in a biologically plausible deep learning model. But it turned out to be an incredibly difficult task, and I realized that even though understanding the human brain would be easier than understanding the entire universe, it was still beyond the scope of a single lifetime, largely due to the difficulty in mapping and recording all ~100 billion individual neurons and ~100 trillion synapses in the human brain. Feeling defeated, I shifted my focus to more immediate needs like figuring out how to turn math into money.

During my sophomore year I jumped to industry, interning as a data scientist at Aunalytics. There were several interesting problems to work on and the CEO and Chief Data Scientist were impressed by my contributions, so I worked full-time throughout my junior and senior years while simultaneously finishing my degree. My projects focused on churn modeling and exploratory data mining in financial services and digital news. However, I noticed that quantitative modeling was most valuable when paired with domain expertise, and although I was grateful to have broken into data science so early, I wasn't excited by any of the fields that were conventionally paired with data science. Instead, I was interested in math education, partly because of my interest in the brain and partly because of how much I enjoyed working as a Mathnasium instructor on evenings and weekends throughout high school and college. So, after graduating from Notre Dame in 2018, I left Aunalytics to throw myself fully into math education and gain more domain expertise while earning my master's in computer science from Georgia Tech.

Math Academy

Initially, I took on a bunch of scattered freelance work in math education: tutoring for many different agencies, developing content for several online learning platforms, and teaching high school and weekend test prep. Gradually, all my work converged at the same organization, Math Academy, a highly accelerated math program for gifted middle and high school students. Math Academy ran on top of educational software built by the founder, Jason Roberts, who wanted to create and commercialize the ultimate online platform for learning grade school through university-level math. I got involved at the core of Math Academy's product during the summer of 2019, when Jason hired me to develop an algorithm that would automatically create personalized assignments while leveraging effective learning techniques like spaced repetition and interleaving. By the end of summer, the project was a success, and it upgraded the software from a teaching tool to a fully automated and personalized learning system. Since then, I've spent nearly every waking hour involved in all aspects of the product's development, with a focus on models & algorithms.

We recently launched the beta version of our expert tutoring system, and it's showing much promise. We have many students who are on track to get a 5 on AP Calculus BC by 8th grade and learn all of undergraduate math before graduating high school. And the crazy part is that these kids aren't even prodigies -- they're just kids with high aptitude and interest in math, who have access to software that allows them to learn an unlimited amount of math in the most efficient way possible. Math Academy is the math learning system that I longed for while growing up, and my goal is to turn it into a household name among families with gifted students.

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Thank you to the following people who have significantly impacted my life:
  • My parents - for always encouraging and wishing the best for me
  • Ken Andrzejewski and Douglas Sisk - for teaching me how to put myself out there, find research opportunities, and compete in science fairs during high school
  • Ilan Levine - for advising my first high school physics research project
  • Dave Cieslak - for hiring me as a data scientist during college and sending interesting projects my way
  • Jason Roberts - for helping me find my niche after college and providing a constant stream of challenge and mentorship