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.

• Human-in-the-loop AI training: 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 smooth manifold theory in Mathlib. Contributor to Rémy Degenne’s open-source formalization of Brownian motion. Currently working towards the formalization of Stokes’ Theorem. Author of Marathon, a framework for semiautonomous textbook formalization with Aristotle and Claude. Author of WikiLean, an annotated mirror of WikiProject Mathematics for Mathlib declarations. Organizer of the NYC Lean meetup. 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).