Kevin Pierre Mott

Kevin Mott, Ph.D.

Assistant Professor of Finance, Teaching Stream
Director of Teaching Innovation, FinHub: The Financial Innovation Lab
Rotman School of Management
University of Toronto

kevin [dot] mott [at] rotman [dot] utoronto [dot] ca
Curriculum Vitæ (PDF)
Faculty Profile
LinkedIn

About

I am an Assistant Professor of Finance in the Teaching Stream at the Rotman School of Management, University of Toronto, where I also serve as Director of Teaching Innovation at FinHub: The Financial Innovation Lab.

I completed my Ph.D. in Financial Economics from Carnegie Mellon University’s Tepper School of Business in 2025. My dissertation was entitled ‘Finance-Informed Neural Networks: Deep Learning for Functional Problems in Macroeconomics and Finance.’

Teaching

My teaching interests include financial markets, asset pricing (derivative securities, fixed income), and macroeconomics. I am particularly passionate about integrating modern computational methods, including machine learning applications, mathematical modeling, and numerical methods into finance education.

University of Toronto

I currently teach:

I am scheduled to teach the following in Winter 2026:

I have previously taught:

Carnegie Mellon University

During my time in graduate school, I taught:

Letters of Recommendation

I am happy to write letters of recommendation for students who meet all of the following criteria:

I do not write letters for students who are currently enrolled in my course, unless you also meet the criteria above.
If you meet these criteria and would like to request a letter, please reach out to discuss your goals at least one month prior to the earliest deadline.

Open-Source Teaching Tool: Creating Teaching Content with AI Tools as Writing Assistants

I explain a flexible workflow framework for creating custom course materials using AI assistants as writing tools. The approach implements an iterative content generation pipeline where instructors retain control of pedagogical structure while delegating drafting, formatting, and iteration to AI. This editor-first methodology leverages modern language models to efficiently produce lecture slides, detailed notes, and practice materials tailored to specific student populations.

The flexible framework allows instructors to easily adapt the methodology to their own courses by customizing student level, examples, technical depth, and exposition style through LaTeX templates and AI configuration files.

GitHub: Creating Teaching Content with AI Tools as Writing Assistants

Research Interests

My research interests lie in two main areas:

  1. Computational methods in mathematical finance, particularly term structure modeling and pricing interest rate derivatives; and
  2. The intersection of quantitative macro-finance and computational methods, with a focus on policy analysis.

Open-Source Research Tool: Finance-Informed Neural Networks for Overlapping Generations Models

I developed a flexible computational framework for solving stochastic overlapping generations models using deep learning. The code implements policy iteration with neural networks that directly incorporate economic constraints (Euler equations, feasibility conditions) into the training process. This grid-free approach leverages GPU acceleration to efficiently solve high-dimensional complex dynamic stochastic general equilibrium models.

Designed with modularity in mind, the modular nature of the program allows researchers to easily adapt the methodology to their own models by customizing model parameters, constraints, and equilibrium conditions.

GitHub: Finance-Informed Neural Networks for OLG Models

Working Papers

Work In Progress

Education

Ph.D. in Financial Economics (2025)
Tepper School of Business, Carnegie Mellon University

M.S. in Financial Economics (2021)
Tepper School of Business, Carnegie Mellon University

B.S. in Mathematics (2019)
Northeastern University