Conor McCoid

Conor McCoid is a Research Fellow in Mathematics at McMaster University, specializing in numerical analysis, scientific computing, and computational geometry. His research focuses on domain decomposition methods, spectral collocation techniques, and algorithm development for computational problems such as phase field models and geometric intersections.

He holds a PhD from the University of Geneva, where he worked on domain decomposition under Martin Gander, and a Master's from Simon Fraser University in spectral collocation methods with Manfred Trummer. His postdoctoral work at McMaster involves accelerating phase field models for fractures and optimizing Schwarz methods.

McCoid's research interests include developing robust algorithms for geometric intersections (e.g., triangles and tetrahedra), improving spectral collocation methods for boundary value problems, and prototyping machine learning algorithms for music analysis (e.g., Mozart classification). His GitHub repository includes code for tetrahedral intersections, adaptive Schwarz methods, and music data processing using tools like music21 and scikit-learn.

His publications explore numerical methods for multigrid cycles, nonlinear solvers, and geometric algorithms, often emphasizing robustness and efficiency in computational contexts. Collaborations include work with Blaise Bourdin (McMaster), Felix Kwok (Université Laval), and Martin Gander (Geneva).