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Daniel N. KaslovskyWebsite of Daniel N. Kaslovsky
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Website of Daniel N. Kaslovsky
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Daniel N. Kaslovsky
http://www.danielkaslovsky.com/teaching
PhD, Applied Mathematics. University of Colorado, Boulder. Department of Applied Mathematics. Spring 2013: APPM 7400 - Recent Topics in Big Data. 2012 Summer School on Geometry and Data. Held jointly by Center for Geometric Analysis and Data, Washington State University, and University of Idaho). Bull; Designed and taught lectures on probability, high-dimensional geometry, and subspace perturbation theory. University of Colorado, Boulder. Department of Applied Mathematics.
Daniel N. Kaslovsky
http://www.danielkaslovsky.com/index
PhD, Applied Mathematics. I received my Ph.D. in applied mathematics from the University of Colorado at Boulder and was awarded a NSF Postdoctoral Fellowship for research at the National Institute of Standards and Technology (NIST). I now develop machine learning algorithms for information/cyber security applications at a technology company in Boulder, Colorado. Information for the reading course I developed is available here: "Recent Topics in Big Data". 10 November 2015 - Daniel N. Kaslovsky.
Daniel N. Kaslovsky
http://www.danielkaslovsky.com/vitae
PhD, Applied Mathematics. Please contact me for detailed information about my current and non-academic positions. Download my academic C.V. here: [ PDF.
Daniel N. Kaslovsky
http://www.danielkaslovsky.com/APPM7400-SPR13
PhD, Applied Mathematics. APPM 7400: Recent Topics in Big Data. Department of Applied Mathematics. University of Colorado, Boulder. Course flier: [ PDF. Thursdays, 4:00 - 5:00 PM. ECCR 257 (APPM Newton Computer Lab). Papers are listed below). APPM Colloquium by Anna Gilbert - ECCR 245, 3:00 PM. Sparsity, Sparse Representation. Sparse Representation and Signal/Image Processing. 6), (7), (8). Low Rank Matrix Approximations. Random Matrix Theory, Randomized Algorithms. Data Models and Manifold Learning.
Daniel N. Kaslovsky
http://www.danielkaslovsky.com/code
PhD, Applied Mathematics. MATLAB code for "Non-Asymptotic Analysis of Tangent Space Perturbation". Download implementation of Algorithm 1 to recover the origin of a local neighborhood from noisy, manifold-valued data:. This material is based upon work supported by the National Science Foundation under Grant Number ACI-1226362.
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Daniel N. Kaslovsky
PhD, Applied Mathematics. I received my Ph.D. in applied mathematics from the University of Colorado at Boulder and was awarded a NSF Postdoctoral Fellowship for research at the National Institute of Standards and Technology (NIST). I now develop machine learning algorithms for information/cyber security applications at a technology company in Boulder, Colorado. Information for the reading course I developed is available here: "Recent Topics in Big Data".
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