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Mikhail Belkin

  • Professor, Statistics
  • Professor, Computer Science & Engineering
  • 597 Dreese Laboratories
    2015 Neil Ave
    Columbus, OH 43210
  • 614-292-5841

Honors

  • 2011

    2011 Lumley Research Award. .

Chapters

2013

  • Xueyuan Zhou, Mikhail Belkin. 2013. "Semi-Supervised Learning." In Academic Press Library in Signal Processing: Signal Processing Theory and Machine Learning, Amsterdam: Elsevier.

Presentations

  • "Understanding Hidden Structure: Manifold Learning from Labeled and Unlabeled Data." 2011, Presented at Ohio State ECE IPS Seminar, Columbus,
  • "Learning Mixtures of Gaussians in High Dimension." 2011, Presented at The Fourth International Conference on computational Harmonic Analysis, Hong Kong,
  • "Clustering, Gaussian Mixture Models, and Sparse Eigenfunction Bases for Semi-Supervised Learning." 2010, Presented at Purdue Computer Science & Machine Learning Colloquium,
  • "Mixture Learning and Eigenfunctions of Convolution Operators." 2010, Presented at 13th International Conference on Approximation Theory, San Antonio,
  • "Learning Gaussian Mixture Distributions." 2010, Presented at UCSD Information Theory and Applications Workshop, San Diego,
  • "Introduction to Machine Learning and Manifold Methods." 2010, Presented at Summer School in Neuroinformatics, Woods Hole,
  • "Spectral Methods in Statistical Learning." 2008, Presented at BIRS workshop on Understanding the New Statistics: Expanding Core Statistical Theory, Banff, CA|CAN
  • "Spectral Methods in Learning." 2008, Presented at 2008 Beijing International Conference on Machine Learning and Data Mining, Beijing, CN|CHN
  • "Manifold Learning." 2008, Presented at 2nd LANL/OSU Workshop, Columbus,
  • "Manifold Laplacians and Machine Learning." 2008, Presented at OSU Department of Mathematics, geometry seminar,
  • "Manifold and Geometric Learning." 2008, Presented at IMA workshop on Multi-Manifold Data Modeling and Applications, Minneapolis,
  • "Learning Mixtures of Gaussians using Spectral Methods." 2008, Presented at FOCM Workshop on Learning Theory, HK|HKG
  • "Geometric View of Machine Learning." 2008, Presented at OSU Center for Cognitive Science Seminar Series, Columbus,
  • "Geometric and spectral methods in learning." 2008, Presented at Carnegie-Mellon University Statistics Colloquium, Pittsburgh,
  • "New developments in Gaussian mixture modelling." 2010, Presented at Oregon State University joint Mathematics/Computer Science Colloquium, Corvallis,
  • "Tutorial on Manifold and Semi-supervised Learning." 2009, Presented at Summer School on Manifold Learning in Image and Signal Analysis, Ven, DK|DNK
  • "Tutorial on Machine Learning." 2009, Presented at Summer School an Neuroinformatics, Woods Hole, US|USA
  • "Towards Understanding Mixtures of Gaussians: Spectral Methods and Polynomial Time Learning with No Separation." 2009, Presented at Forum on Geometric Aspects of Machine Learning and Visual Analytics: Recent Developments and Future Challenges, Atlantic City,
  • "Statistical Aspects og High-dimensional data." 2009, Presented at Peking University Statistics Department Colloquium, Beijing, CN|CHN
  • "Statistical Aspects og High-dimensional data." 2009, Presented at Zhijiang University Computer Science, Zhejiang, CN|CHN
  • "Semi-supervised Learning using Sparse Eigenfunction Bases and Gaussian Mixture Models." 2009, Presented at Center for Imaging Science Colloquium, John Hopkins University, Baltimore,
  • "Manifold and Semi-supervised Learning and Applications." 2009, Presented at 1st Sino-USA Summer School in Vision, Learning and Pattern Recognition, Beijing, CN|CHN
  • "Manifold and Semi-supervised Learning." 2009, Presented at Los Alamos Seminar, Los Alamos,
  • "Manifold and Semi-supervised Learning." 2009, Presented at Microsoft Research Asia, Beijing, CN|CHN
  • "Learning mixtures of Gaussians using Convolution Operators." 2009, Presented at Information Theory and Applications Workshop, ITA 2009, San Diego,
  • "Learning Gaussian Mixture Distributions in Hign Dimension." 2009, Presented at Seoul National University Statistics Colloquium, Seoul,
  • "Geometry and Learning." 2009, Presented at Duke University, Computer Science Algorithms Seminar,
  • "Clustering, sparsity and semi-supervised learning." 2009, Presented at NIPS Workshop on Manifolds, Sparsity, and Structured models, Whistler, CA|CAN
  • "Supervised and Unsupervised Learning in High Dimension." 2010, Presented at OSU Center for Cognitive Science Cogfest 2010, Columbus,
  • "Geometrical Methods and Data Analysis." 2010, Presented at Laboratoire J.-V. Poncelet, Moscow,
  • "Learning Mixtures of Gaussians in High Dimension." 2011, Presented at MIT Brains & Machines Seminar, Boston,
  • "Manifold learning, the heat equation and spectral clustering." 2011, Presented at Statistical Learning Theory and Applications (MIT CBCL course), Boston,
  • "Spectral Clustering and Semi-supervised Learning." 2006, Presented at WNAR/IMS meeting, Flagstaff,
  • "Semi-supervised Learning." 2006, Presented at 21st European Conference on Operational Research 2006, Reykjavik, IS|ISL
  • "Geometry and Manifold Structures in Statistical Learning." 2006, Presented at Workshop on New Perspectives in Geometric Analysis, Toledo,
  • "Geometric View of Machine Learning." 2006, Presented at OSU Statistics Colloquium, Columbus,
  • "Estimating Surface Volumes of Convex Bodies." 2006, Presented at American Mathematical Society Sectional Meeting, Cincinnati,
  • "Aspects of Manifold and Statistical Learning." 2006, Presented at Joint Statistical Meeting, Seattle,
  • "Aspects of Manifold and Statistical Learning." 2006, Presented at Institute of Mathematical Statistics Annual Meeting, Rio de Janeiro, BR|BRA
  • "Learning Mixture of Gaussians using Spectral Methods." 2007, Presented at Workshop on Geometric and Topological Approaches to Data Analysis, Chicago,
  • "Learning from Labeled and Unlabeled Data." 2007, Presented at Industrial and Systems Engineering Seminar series, Columbus,
  • "Learning from labeled and unlabeled data." 2007, Presented at The North Carolina State University, EECS Seminar,
  • "Geometry and Learning." 2007, Presented at 56th Session of the International Statistical Institute, Lisbon, PT|PRT
  • "Aspects of Manifold and Statistical Learning." 2007, Presented at Conference on Applied Inverse Problems (AIP) 2007, Vancouver, CA|CAN
  • "Aspects of Manifold and Statistical Learning." 2007, Presented at SIAM Conference on Applications of Dynamical Systems, Snowbird,
  • "Aspects of Manifold and Statistical Learning." 2007, Presented at 6th International Congress on Industrial & Applied Mathematics, Zurich, CH|CHE
  • "Aspects of Manifold and Statistical Learning." 2007, Presented at PASCAL Workshop on Graph Theory and Machine Learning, Bled, SI|SVN
  • "Aspects of Manifold and Statistical Learning." 2007, Presented at Information Theory and Applications Workshop, San Diego,
  • "Learning Gaussian Mixture Models." 2011, Presented at Dagstuhl Workshop on Mathematical and Computational Foundations of Learning Theory,
  • "An introduction to machine learning." 2011, Presented at Summer School an Neuroinformatics, Wood Hole,
  • "Learning Mixtures of Gaussians and Other Distributions." 2012, Presented at Colloquium at Inria, Saclay, FR|FRA
  • "Algebraic-Geometric Methods for Learning Gaussian Mixture Models." 2012, Presented at Workshop on Algebraic Statistics, IST Austria,
  • "Toward understanding complex spaces: graph Laplacians on manifolds with singularities and boundaries." 2012, Presented at AMS Sectional Meeting, Special Session on Applied Topology, Akron,
  • "Algebraic-Geometric Methods for Learning Mixtures of Gaussians and Other Distributions." 2012, Presented at BIRS Workshop on Topological Data Analysis and Machine Learning Theory, CA|CAN
  • "Inverse Density as an Inverse Problem." 2013, Presented at Seminar at Johann Radon Institute for Computational and Applied Mathematics, AT|AUT
  • "Inverse Density as an Inverse Problem via Fredholm Machines." 2013, Presented at Colloquium at the Italian Institute of Technology, Genova, IT|ITA
  • "Dealing with Singular Manifolds:Theory and Applications." 2012, Presented at Oberwolfach workshop on Learning Theory and Approximation,,
  • "Machine Learning and Differential Geometry of the Data." 2012, Presented at Colloquium at the Mathematical Sciences Center, Tsinghua University,
  • "Algebraic geometry for learning high-dimensional mixtures of Gaussians and other distributions." 2012, Presented at Nonparametric and high-dimensional statistics, FR|FRA
  • "Inverse Density as an Inverse Problem." 2013, Presented at International Conference on Approximation Theory and Applications, HK|HKG
  • "Understanding geometry of the data." 2010, Presented at International Conference on Geometry/Imaging,
  • "Machine Learning and Differential Geometry of the Data." 2012, Presented at Yaroslavl International Conference on Discrete Geometry, Yaroslavl, RU|RUS
  • "Inverse Density as an Inverse Problem." 2013, Presented at MPI for Biological Cybernetics, Tubingen, DE|DEU
  • "Inverse Density Estimation using Fredholm Machines." 2013, Presented at Spectral Learning Workshop, ICML 2013,
  • "Features and Representations." 2013, Presented at Workshop on Learning Data Representation: Hierarchies and Invarianc,
  • "Manifold and Semi-supervised Learning." 2013, Presented at Case Western CS Colloquium,
  • "Machine Learning and the Differential Geometry of the Data." 2014, Presented at Conference on Numerical Analysis and Scientific Computing, Leipzig, DE|DEU
  • "The Hidden Convexity of Spectral Clustering." 2014, Presented at Information Theory and Applications,
  • "Eigenvectors of orthogonally decomposable functions." 2017, Presented at Oberwofach Workshop on Statistical Recovery of Discrete, Geometric and Invariant Structures,

Papers in Proceedings

2016

  • Mikhail Belkin, Luis Rademacher, James Voss "Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive." JOURNAL: "Proceedings of COLT 2016." in COLT 2016. (6 2016).