Talks

  1. Interpolation for Physical Big Data. Colloquium, University of Minnesota. February 26, 2015. pdf.
  2. Interpolation for Physical Big Data. Colloquium, City College of New York. February 18, 2015. pdf.
  3. High dimensional learning rather than computing in quantum chemistry. Applied Mathematics Seminar, Yale University. February 4, 2015. pdf.
  4. Interpolation for Physical Big Data. Colloquium, Michigan State University. January 16, 2015. pdf.
  5. High dimensional learning rather than computing in quantum chemistry. Joint Mathematics Meetings, AMS Session on Numerical Analysis and Computer Science, San Antonio, Texas. January 11, 2015. pdf.
  6. High dimensional learning rather than computing in quantum chemistry. Foundations of Computational Mathematics Conference 2014, Workshop A2: Computational Harmonic Analysis, Image and Signal Processing, Universidad de la República. December 12, 2014. pdf.
  7. Minimal C^{1,1} Extensions. 5th International Conference on Computational Harmonic Analysis, Vanderbilt University. May 23, 2014. zip.
  8. Minimal C^{1,1} Extensions. Analyse non-linéaire et EDP seminar, Institut Henri Poincaré. April 15, 2014. zip.
  9. Diffusion based manifold learning (joint talk with Guy Wolf). Sierra group meeting, École normale supériure. October 23, 2013. pdf.
  10. Diffusion maps for changing data. Statistics, Mathematics and Applications Conference, Fréjus, France. September 3, 2013. pdf.
  11. Diffusion geometry for high dimensional data. Smorgasbord Seminar, Cornell University. July 3, 2013. pdf.
  12. Quasi absolutely minimal Lipschitz extensions. Analysis Seminar, Yale University. February 21, 2013. pdf.
  13. New developments in the theory of absolutely minimal Lipschitz extensions. Analysis Seminar, Cornell University. December 3, 2012. pdf.
  14. Diffusion maps for changing data. Colloquium, Kansas State University. November 29, 2012. pdf.
  15. Diffusion maps for changing data. Image Analysis Seminar, University of Houston. November 5, 2012. pdf.
  16. Diffusion maps for changing data. Computational Analysis Seminar, Vanderbilt University. October 17, 2012. pdf.
  17. Diffusion maps for changing data. Norbert Wiener Center Seminar, University of Maryland. October 2, 2012. pdf.
  18. A general theorem of existence of quasi absolutely minimal Lipschitz extensions. Workshop on Whitney type extension and trace problems, The Fields Institute. August 28, 2012. video (Fields website).
  19. Diffusion maps for changing data. Mathematics Colloquium and Informal Seminar, Bell Labs. July 26, 2012. pdf.
  20. Diffusion maps for changing data. Operator Algebras, Frames, and Undergraduate Research: A Conference in Honor of the 70th Birthday of David R. Larson, Texas A&M University. July 21, 2012. pdf.
  21. Diffusion maps for changing data. Applied Mathematics Seminar, Duke University. January 23, 2012. pdf.
  22. Minimal interpolants in C^{1,1}(R^n). Groupe de travail “applications des mathématiques,” École normale supérieure de Cachan, Antenne de Bretagne, France. December 7, 2011. pdf.
  23. Wells’ construction of interpolants in C^{1,1}(R^n). Fourth Whitney Problems Workshop, College of William and Mary. August 4, 2011. zip.
  24. Sparse endmember extraction and demixing. Applied Mathematics Seminar, Yale University. October 6, 2009. pdf.
  25. Harmonic frames of prime order. Mini-Conference in Harmonic Analysis on the Occasion of John Benedetto’s 70th Birthday, University of Maryland. August 21, 2009. pdf.
  26. Enumeration of harmonic frames and frame based dimension reduction. PhD Final Oral Examination, University of Maryland. July 24, 2009. pdf.
  27. Frame potential classification algorithm for retinal data. Multispectral Retinal Imaging and Mapping of Naturally Occurring Fluorophore and Chromophore Distributions Research Interaction Team, University of Maryland. May 11, 2009. pdf.
  28. Frame based kernel methods for hyperspectral imagery data. Recent Advances in Harmonic Analysis and Elliptic Partial Differential Equations (conference), University of Virginia. May 9, 2009. pdf.
  29. Frame based kernel methods for hyperspectral imagery data. Graduation Conference 2009, University of Maryland. May 1, 2009. pdf.
  30. Uncertainty principles in sparse representation and compressed sensing. Norbert Wiener Center Seminar, University of Maryland. November 8, 2007. pdf.
  31. Uncertainty principles for finite abelian groups. Norbert Wiener Center Seminar, University of Maryland. September 20, 2007. pdf.
  32. Mock Fourier series for the standard Cantor measure. Mathematical Association of America Mathfest, Burlington, Vermont. August 2, 2002.
© Matthew Hirn 2013-2015