Florian Schaefer

Florian Schaefer is an Assistant Professor in the Department of Computer Science at New York University. His research focuses on numerical computation, statistical inference, and competitive games. Schaefer received his PhD in applied and computational mathematics from Caltech, where he worked with Houman Owhadi. His work has led to state-of-the-art solvers for elliptic PDEs and efficient algorithms for multi-agent optimization. His research has found applications in materials science, turbulence modeling, and computational geometry. Schaefer is currently hiring PhD students and postdocs in his research area.

Florian Schaefer is a computational mathematician whose research focuses on computational mathematics, partial differential equations, machine learning, and game theory. His work explores the intersection of these fields, particularly in developing efficient numerical methods for solving complex mathematical problems. Schaefer's research includes sparse Cholesky factorization, competitive gradient descent, and Bayesian transport maps for high-dimensional spatial fields. He has also contributed to machine learning through competitive physics informed networks and reinforcement learning approaches. His publications emphasize scalable algorithms, information geometric regularization, and variational methods for Gaussian processes, highlighting his commitment to advancing computational techniques for scientific and engineering applications.