Max Mercer

Max Mercer earned a PhD in Applied Mathematics under Nicolas Lanchier at Arizona State University, where he studied the sensitivity of particle systems to local dynamics. He was a teaching assistant at ASU in the School of Mathematical and Statistical Sciences since the Fall of 2021, helping wonderful students learn calculus, linear algebra, differential equations, discrete math, stochastic methods, neural networks and machine learning, scientific computing, and more. He joined the department as a full-time assistant teaching professor in the Fall 2025 semester.

His primary research focuses on the long-term behavior of spatially-explicit epidemiological and sociological models. He also has diverse secondary interests:

• High-dimensional constrained optimization techniques.
• Nonlinear feature space dimensionality reduction algorithms (particularly t-SNE).
• Numerical methods for solving partial differential equations, especially hyperbolic PDEs.
• Unsupervised machine learning, with an emphasis on linguistic clustering.
• Number theory applications, such as solving systems of Diophantine equations for construction and supply chain management.

He aims to use mathematics to create accurate, useful models for real-life dynamical systems. He has authored reference books, created a diverse clustering algorithm, and worked with a team to build a neural network which recognizes emotions from facial expression images.

Dr. Mercer extends his deepest gratitude to his phenomenal advisor, Dr. Nicolas Lanchier, as well as the Krepper family, whose generous sponsorship through the ARCS Foundation made his PhD research possible.