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I am an Associate Professor of Mathematics at the University of Minnesota. My research involves interactions between partial differential equations (PDE), numerical analysis, applied probability, and computer science. I am interested in both the rigorous analysis of PDE, and the development and implementation of algorithms.
Jeff Calder. Associate Professor, University of Minnesota. Verified email at umn.edu - Homepage. ... J Calder, N García Trillos, M Lewicka. SIAM Journal on Mathematical Analysis 54 (1), 1169-1222, 2022. 33: 2022: Rates of convergence for Laplacian semi-supervised learning with low labeling rates.
The overall goal of my research is to use the theory of partial differential equations (PDEs) and the calculus of variations to study foundational problems in machine learning and data science, and develop new, more efficient, algorithms founded on strong theoretical principles. Much of my work involves proving large sample size continuum ...
The overall goal of my research is to use the theory of partial differential equations (PDEs) and the calculus of variations to study foundational problems in machine learning and data science, and develop new, more efficient, algorithms founded on strong theoretical principles.
Jeff Calder is an Associate Professor of Mathematics at the University of Minnesota. His research involves interactions between partial differential equations (PDE), numerical schemes, applied probability, and computer science.
School of Mathematics. University of Minnesota. 538 Vincent Hall. Phone: 612-626-1324. Email: jwcalder at umn dot edu. Publications. J. Calder and N. Drenska. Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian.
My research involves interactions between partial differential equations (PDE), numerical schemes, applied probability, and computer science. I am interested in both the rigorous analysis of PDE, and the development and implementation of algorithms.
