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 current research interests include the long-term behavior of spatially-explicit epidemiological and sociological models, with secondary interests in the application of high-dimensional constrained optimization techniques, nonlinear feature space reduction and dimensionality-reduction algorithms (especially t-SNE), the development of numerical methods for solving (especially hyperbolic) partial differential equations, unsupervised machine learning algorithms (especially clustering algorithms in linguistics), and number theory tools (especially related to solving systems of Diophantine equations in construction and supply chain management). His goal is 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.

He thanks his phenomenal advisor, Dr. Nicolas Lanchier, and the Krepper family who generously sponsored his PhD research with the ARCS Foundation.