
About
Gary L. Miller is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. His research focuses on Spectral Graph Theory, Algorithms, Computational Geometry, and Scientific Computing. He has developed influential methods in graph partitioning, mesh generation, and numerical linear algebra solvers.
Teaching includes advanced courses like Spectral Graph Theory, Algorithms, and Computational Geometry. Active in publishing, recent work involves weighted Cheeger inequalities, exact manifold metric computations, and adaptive graph sketching techniques. Projects include Orasis, 3D Meshing Software, Tumble, and Sangria.
His work bridges theoretical foundations with applications in machine learning, image processing, and scientific computing. Current projects emphasize efficient graph algorithms and scalable numerical methods.
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