Jack McCarthy

MA in Mathematics · 3.9 GPA

Real Analysis, Complex Analysis, Geometry & Topology, Algebra.

BS in Computer Science & Mathematics, Minor in Physics · 3.54 GPA

Numerical Methods, Differential Equations, Algorithms & Data Structures, Electricity & Magnetism, Fluid Mechanics, Classical Mechanics.

Graduate of exchange program with the African Leadership Academy.

• Solve user-generated problems in differential geometry and stochastic differential equations.
• Design novel math problems to test and train AI models for clients including Alphabet Inc. and OpenAI.
• Promoted internally to senior reviewer on reinforcement-learning project datasets.

• Graded two sections of Physics lab reports and three sections of Arithmetic & Algebra homework and exams.
• Applied algebra, optics, electrical circuits, and atomic physics to diagnose student mistakes.
• Wrote individualized feedback and adapted to ongoing feedback from professors.

• Full-time faculty member at the oldest boarding school in the United States.
• Taught five high-school classes: Algebra I, Algebra II, Computer Science, and Discrete Mathematics.
• Owned lesson prep, curriculum planning, homework and test design, faculty meetings.
• Organized student clubs, spearheaded an AI mentorship program, led the coding team to win a local hackathon.

• Full-time wait staff position on a team of six at a fine-dining Italian restaurant.
• Customer service, multitasking, and maintaining a calm disposition under pressure.

Math Formalization in Lean. Contributor to the formalization of Lie geometry in Mathlib. Contributor to Rémy Degenne’s open-source formalization of Brownian motion. Currently working towards Perelman’s proof of the Poincaré conjecture with Bennett Chow of UCSD alongside three graduate students. Experimenting with auto-formalization in Claude Opus. Delivered the talk “4 Reasons You Should Care About Math Formalization” at Wikipedia Day 2026.

WFM Team at Northeast Ohio Medical University. Lead engineer for a granted research team alongside four medical students. Designed, prototyped, and presented a patented medical device to improve cancer patient outcomes. Winner of the legacy team award at Neovations 2025. Applied programming, fluid mechanics, and electrical engineering.

AI and Statistical Models. Implemented image-classification CNNs, FFNs, and genetic algorithms in TensorFlow. Wrote ADAM backpropagation from scratch in NumPy. Completed Andrew Ng’s Neural Networks and Deep Learning specialization with >90% on all exams and projects. Implemented Brownian-bridge stochastic simulations in MATLAB.

Self-Studying Mathematics. Scored 11 on the 2025 Putnam exam. Contributor to WikiProject Mathematics. Worked through every problem in the following textbooks: Linear Algebra (Friedberg, Insel & Spence); Visual Differential Geometry (Needham); Numerical Solutions to SDEs Ch. 1–4 (Kloeden & Platen); Basic Topology Ch. 1–4 (Armstrong); A Comprehensive Introduction to Differential Geometry Ch. 1–2 (Spivak); Introduction to Smooth Manifolds (Lee).