## 2019 |

Liao, Wenjing; Maggioni, Mauro Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data Journal Article Journal of machine learning Research, 20 (98), pp. 1-63, 2019. Links | BibTeX | Tags: geometric wavelets, Machine learning, multiscale analysis, statistics @article{LiaoMaggioni:GMRA, title = {Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data}, author = {Wenjing Liao and Mauro Maggioni}, url = {http://jmlr.org/papers/v20/17-252.html}, year = {2019}, date = {2019-01-01}, journal = {Journal of machine learning Research}, volume = {20}, number = {98}, pages = {1-63}, keywords = {geometric wavelets, Machine learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {article} } |

## 2017 |

Gerber, Sam; Maggioni, Mauro Multiscale Strategies for Discrete Optimal Transport Journal Article Journ. Mach. Learn. Res., (72), pp. 1–32, 2017. Links | BibTeX | Tags: multiscale analysis, optimal transport, optimization @article{GM:mop, title = {Multiscale Strategies for Discrete Optimal Transport}, author = {Sam Gerber and Mauro Maggioni}, url = {https://jmlr.csail.mit.edu/papers/volume18/16-108/16-108.pdf}, year = {2017}, date = {2017-01-01}, journal = {Journ. Mach. Learn. Res.}, number = {72}, pages = {1–32}, keywords = {multiscale analysis, optimal transport, optimization}, pubstate = {published}, tppubtype = {article} } |

Little, Anna V; Maggioni, Mauro; Rosasco, Lorenzo Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature Journal Article Applied and Computational Harmonic Analysis, 43 (3), pp. 504 – 567, 2017, (Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310). BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics @article{LMR:MGM1, title = {Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature}, author = {Anna V Little and Mauro Maggioni and Lorenzo Rosasco}, year = {2017}, date = {2017-01-01}, journal = {Applied and Computational Harmonic Analysis}, volume = {43}, number = {3}, pages = {504 – 567}, note = {Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310}, keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {article} } |

## 2013 |

Gerber, S; Maggioni, Mauro Multiscale dictionaries, transforms, and learning in high-dimensions Inproceedings Proc. SPIE conference Optics and Photonics, 2013. BibTeX | Tags: dictionary learning, imaging, Machine learning, multiscale analysis @inproceedings{GM_MultiscaleDictionariesSPIE, title = {Multiscale dictionaries, transforms, and learning in high-dimensions}, author = {S Gerber and Mauro Maggioni}, year = {2013}, date = {2013-01-01}, booktitle = {Proc. SPIE conference Optics and Photonics}, keywords = {dictionary learning, imaging, Machine learning, multiscale analysis}, pubstate = {published}, tppubtype = {inproceedings} } |

Iwen, Mark A; Maggioni, Mauro Approximation of points on low-dimensional manifolds via random linear projections Journal Article Inference and Information, 2 (1), pp. 1–31, 2013, (arXiv:1204.3337v1, 2012). BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis @article{IM:GMRA_CS, title = {Approximation of points on low-dimensional manifolds via random linear projections}, author = {Mark A Iwen and Mauro Maggioni}, year = {2013}, date = {2013-01-01}, journal = {Inference and Information}, volume = {2}, number = {1}, pages = {1–31}, note = {arXiv:1204.3337v1, 2012}, keywords = {Machine learning, Manifold Learning, multiscale analysis}, pubstate = {published}, tppubtype = {article} } |

## 2012 |

Bouvrie, Jake; Maggioni, Mauro Geometric Multiscale Reduction for Autonomous and Controlled Nonlinear Systems Inproceedings IEEE Conference on Decision and Control (CDC), 2012. BibTeX | Tags: control theory, geometric wavelets, Machine learning, multiscale analysis @inproceedings{BM_GMReductionControlledSystems, title = {Geometric Multiscale Reduction for Autonomous and Controlled Nonlinear Systems}, author = {Jake Bouvrie and Mauro Maggioni}, year = {2012}, date = {2012-01-01}, booktitle = {IEEE Conference on Decision and Control (CDC)}, keywords = {control theory, geometric wavelets, Machine learning, multiscale analysis}, pubstate = {published}, tppubtype = {inproceedings} } |

Bouvrie, Jake; Maggioni, Mauro Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning Journal Article 2012. Links | BibTeX | Tags: Machine learning, multiscale analysis, reinforcement learning, representation learning, spectral graph theory, transfer learning @article{BM:MMDPs, title = {Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning}, author = {Jake Bouvrie and Mauro Maggioni}, url = {http://arxiv.org/abs/1212.1143}, year = {2012}, date = {2012-01-01}, keywords = {Machine learning, multiscale analysis, reinforcement learning, representation learning, spectral graph theory, transfer learning}, pubstate = {published}, tppubtype = {article} } |

Allard, William K; Chen, Guangliang; Maggioni, Mauro Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis Journal Article Applied and Computational Harmonic Analysis, 32 (3), pp. 435–462, 2012. BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics @article{CM:MGM2, title = {Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis}, author = {William K Allard and Guangliang Chen and Mauro Maggioni}, year = {2012}, date = {2012-01-01}, journal = {Applied and Computational Harmonic Analysis}, volume = {32}, number = {3}, pages = {435–462}, keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {article} } |

## 2011 |

Chen, Guangliang; Little, Anna V; Maggioni, Mauro Multi-Resolution Geometric Analysis for data in high dimensions Journal Article Proc. FFT 2011, 2011. BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis @article{FFT2011, title = {Multi-Resolution Geometric Analysis for data in high dimensions}, author = {Guangliang Chen and Anna V Little and Mauro Maggioni}, year = {2011}, date = {2011-08-01}, journal = {Proc. FFT 2011}, keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis}, pubstate = {published}, tppubtype = {article} } |

Maggioni, Mauro Multiscale Geometric Dictionaries for Point-Cloud Data, Presented at SPARS 11, http://www.math.duke.edu/~mauro/research.html#Talks Journal Article 2011, (Presented at SPARS 11). Links | BibTeX | Tags: dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis @article{MM:Spars11talk, title = {Multiscale Geometric Dictionaries for Point-Cloud Data, Presented at SPARS 11, http://www.math.duke.edu/~mauro/research.html#Talks}, author = {Mauro Maggioni}, url = {http://www.math.duke.edu/~mauro/research.html#Talks}, year = {2011}, date = {2011-06-01}, note = {Presented at SPARS 11}, keywords = {dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis}, pubstate = {published}, tppubtype = {article} } |

Chen, Guangliang; Maggioni, Mauro Multiscale Geometric Dictionaries for Point-Cloud Data Inproceedings Proc. SampTA, 2011. BibTeX | Tags: dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis @inproceedings{CM:SamptaGW, title = {Multiscale Geometric Dictionaries for Point-Cloud Data}, author = {Guangliang Chen and Mauro Maggioni}, year = {2011}, date = {2011-01-01}, booktitle = {Proc. SampTA}, keywords = {dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis}, pubstate = {published}, tppubtype = {inproceedings} } |

Chen, Guangliang; Maggioni, Mauro Multiscale Geometric and Spectral Analysis of Plane Arrangements Inproceedings Conference on Computer Vision and Pattern Recognition, 2011. BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis @inproceedings{CM:CVPR2011, title = {Multiscale Geometric and Spectral Analysis of Plane Arrangements}, author = {Guangliang Chen and Mauro Maggioni}, year = {2011}, date = {2011-01-01}, booktitle = {Conference on Computer Vision and Pattern Recognition}, keywords = {Machine learning, Manifold Learning, multiscale analysis}, pubstate = {published}, tppubtype = {inproceedings} } |

## 2010 |

Jones, Peter W; Maggioni, Mauro; Schul, Raanan Universal local manifold parametrizations via heat kernels and eigenfunctions of the Laplacian Journal Article Ann. Acad. Scient. Fen., 35 , pp. 1–44, 2010, (http://arxiv.org/abs/0709.1975). BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @article{jms:UniformizationEigenfunctions2, title = {Universal local manifold parametrizations via heat kernels and eigenfunctions of the Laplacian}, author = {Peter W Jones and Mauro Maggioni and Raanan Schul}, year = {2010}, date = {2010-01-01}, journal = {Ann. Acad. Scient. Fen.}, volume = {35}, pages = {1–44}, note = {http://arxiv.org/abs/0709.1975}, keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory}, pubstate = {published}, tppubtype = {article} } |

Chen, Guangliang; Maggioni, Mauro Multiscale Geometric Methods for Data Sets III: multiple planes Journal Article in preparation, 2010. BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis, statistics @article{CM:MGM3, title = {Multiscale Geometric Methods for Data Sets III: multiple planes}, author = {Guangliang Chen and Mauro Maggioni}, year = {2010}, date = {2010-01-01}, journal = {in preparation}, keywords = {Machine learning, Manifold Learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {article} } |

Chen, Guangliang; Maggioni, Mauro Multiscale Geometric Wavelets for the Analysis of Point Clouds Journal Article Proc. CISS 2010, 2010. BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, spectral graph theory @article{CM:geometricwaveletsciss, title = {Multiscale Geometric Wavelets for the Analysis of Point Clouds}, author = {Guangliang Chen and Mauro Maggioni}, year = {2010}, date = {2010-01-01}, journal = {Proc. CISS 2010}, keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, spectral graph theory}, pubstate = {published}, tppubtype = {article} } |

## 2009 |

Little, Anna V; Jung, Y -M; Maggioni, Mauro Multiscale Estimation of Intrinsic Dimensionality of Data Sets Inproceedings Proc. A.A.A.I., 2009. BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis, statistics @inproceedings{MM:MultiscaleDimensionalityEstimationAAAI, title = {Multiscale Estimation of Intrinsic Dimensionality of Data Sets}, author = {Anna V Little and Y -M Jung and Mauro Maggioni}, year = {2009}, date = {2009-01-01}, booktitle = {Proc. A.A.A.I.}, keywords = {Machine learning, Manifold Learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {inproceedings} } |

Little, Anna V; Lee, J; Jung, Y -M; Maggioni, Mauro Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale $SVD$ Inproceedings Proc. S.S.P., 2009. BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis, statistics @inproceedings{MM:MultiscaleDimensionalityEstimationSSP, title = {Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale $SVD$}, author = {Anna V Little and J Lee and Y -M Jung and Mauro Maggioni}, year = {2009}, date = {2009-01-01}, booktitle = {Proc. S.S.P.}, keywords = {Machine learning, Manifold Learning, multiscale analysis, statistics}, pubstate = {published}, tppubtype = {inproceedings} } |

## 2008 |

Coifman, Ronald R; Maggioni, Mauro Geometry Analysis and Signal Processing on Digital Data, Emergent Structures, and Knowledge Building Miscellaneous SIAM News, 2008. BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @misc{CM:SiamNews, title = {Geometry Analysis and Signal Processing on Digital Data, Emergent Structures, and Knowledge Building}, author = {Ronald R Coifman and Mauro Maggioni}, year = {2008}, date = {2008-11-01}, howpublished = {SIAM News}, keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory}, pubstate = {published}, tppubtype = {misc} } |

Maggioni, Mauro; Mhaskar, Hrushikesh Diffusion polynomial frames on metric measure spaces Journal Article ACHA, 3 , pp. 329–353, 2008. BibTeX | Tags: approximation theory, diffusion geometry, heat kernels, Laplacian eigenfunctions, multiscale analysis @article{MM:DiffusionPolynomialFrames, title = {Diffusion polynomial frames on metric measure spaces}, author = {Mauro Maggioni and Hrushikesh Mhaskar}, year = {2008}, date = {2008-05-01}, journal = {ACHA}, volume = {3}, pages = {329–353}, keywords = {approximation theory, diffusion geometry, heat kernels, Laplacian eigenfunctions, multiscale analysis}, pubstate = {published}, tppubtype = {article} } |

Jones, Peter W; Maggioni, Mauro; Schul, Raanan Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels Journal Article Proc. Nat. Acad. Sci., 105 (6), pp. 1803–1808, 2008. BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @article{jms:UniformizationEigenfunctions, title = {Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels}, author = {Peter W Jones and Mauro Maggioni and Raanan Schul}, year = {2008}, date = {2008-02-01}, journal = {Proc. Nat. Acad. Sci.}, volume = {105}, number = {6}, pages = {1803–1808}, keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory}, pubstate = {published}, tppubtype = {article} } |

## 2007 |

Coifman, Ronald R; Maggioni, Mauro Multiscale Data Analysis with Diffusion Wavelets Journal Article Proc. SIAM Bioinf. Workshop, Minneapolis, 2007. BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @article{CM:MsDataDiffWavelets, title = {Multiscale Data Analysis with Diffusion Wavelets}, author = {Ronald R Coifman and Mauro Maggioni}, year = {2007}, date = {2007-04-01}, journal = {Proc. SIAM Bioinf. Workshop, Minneapolis}, keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems}, pubstate = {published}, tppubtype = {article} } |

## 2006 |

Coifman, Ronald R; Maggioni, Mauro Diffusion Wavelets Journal Article Appl. Comp. Harm. Anal., 21 (1), pp. 53–94, 2006, ((Tech. Rep. YALE/DCS/TR-1303, Yale Univ., Sep. 2004)). BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @article{CMDiffusionWavelets, title = {Diffusion Wavelets}, author = {Ronald R Coifman and Mauro Maggioni}, year = {2006}, date = {2006-07-01}, journal = {Appl. Comp. Harm. Anal.}, volume = {21}, number = {1}, pages = {53–94}, note = {(Tech. Rep. YALE/DCS/TR-1303, Yale Univ., Sep. 2004)}, keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems}, pubstate = {published}, tppubtype = {article} } |

Bremer, James Jr. C; Coifman, Ronald R; Maggioni, Mauro; Szlam, Arthur D Diffusion Wavelet Packets Journal Article Appl. Comp. Harm. Anal., 21 (1), pp. 95–112, 2006, ((Tech. Rep. YALE/DCS/TR-1304, 2004)). BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @article{DiffusionWaveletPackets, title = {Diffusion Wavelet Packets}, author = {James Jr. C Bremer and Ronald R Coifman and Mauro Maggioni and Arthur D Szlam}, year = {2006}, date = {2006-07-01}, journal = {Appl. Comp. Harm. Anal.}, volume = {21}, number = {1}, pages = {95–112}, note = {(Tech. Rep. YALE/DCS/TR-1304, 2004)}, keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems}, pubstate = {published}, tppubtype = {article} } |

Coifman, Ronald R; Maggioni, Mauro Multiscale Analysis of Document Corpora Unpublished 2006, (Technical Report). BibTeX | Tags: Machine learning, Manifold Learning, multiscale analysis, Unsupervised Learning @unpublished{CM:MultiscaleAnalysisOfDocumentCorpora, title = {Multiscale Analysis of Document Corpora}, author = {Ronald R Coifman and Mauro Maggioni}, year = {2006}, date = {2006-01-01}, note = {Technical Report}, keywords = {Machine learning, Manifold Learning, multiscale analysis, Unsupervised Learning}, pubstate = {published}, tppubtype = {unpublished} } |

## 2005 |

Coifman, Ronald R; Lafon, S; Lee, A B; Maggioni, Mauro; Nadler, B; Warner, Frederick; Zucker, Steven W Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods Journal Article Proceedings of the National Academy of Sciences of the United States of America, 102 (21), pp. 7432–7438, 2005. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @article{DiffusionPNAS2, title = {Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods}, author = {Ronald R Coifman and S Lafon and A B Lee and Mauro Maggioni and B Nadler and Frederick Warner and Steven W Zucker}, year = {2005}, date = {2005-01-01}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {102}, number = {21}, pages = {7432–7438}, keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems}, pubstate = {published}, tppubtype = {article} } |

Maggioni, Mauro; Bremer, James Jr. C; Coifman, Ronald R; Szlam, Arthur D Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs Conference 5914 (1), SPIE, San Diego, CA, USA, 2005. Links | BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory @conference{MBCS:BiorthogonalDiffusionWavelets, title = {Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs}, author = {Mauro Maggioni and James Jr. C Bremer and Ronald R Coifman and Arthur D Szlam}, editor = {Manos Papadakis and Andrew F Laine and Michael A Unser}, url = {http://link.aip.org/link/?PSI/5914/59141M/1}, year = {2005}, date = {2005-01-01}, journal = {Wavelets XI}, volume = {5914}, number = {1}, pages = {59141M}, publisher = {SPIE}, address = {San Diego, CA, USA}, keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory}, pubstate = {published}, tppubtype = {conference} } |

Szlam, Arthur D; Maggioni, Mauro; Coifman, Ronald R; Bremer, James Jr. C Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions Conference 5914-1 , SPIE, San Diego, CA, USA, 2005. Links | BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory @conference{MSCB:MultiscaleManifoldMethods, title = {Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions}, author = {Arthur D Szlam and Mauro Maggioni and Ronald R Coifman and James Jr. C Bremer}, editor = {Manos Papadakis and Andrew F Laine and Michael A Unser}, url = {http://link.aip.org/link/?PSI/5914/59141D/1}, year = {2005}, date = {2005-01-01}, journal = {Wavelets XI}, volume = {5914-1}, pages = {59141D}, publisher = {SPIE}, address = {San Diego, CA, USA}, keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory}, pubstate = {published}, tppubtype = {conference} } |

## 2004 |

Coifman, Ronald R; Maggioni, Mauro Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms Technical Report Dept. Comp. Sci., Yale University (YALE/DCS/TR-1289), 2004. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @techreport{CMTech, title = {Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms}, author = {Ronald R Coifman and Mauro Maggioni}, year = {2004}, date = {2004-05-01}, number = {YALE/DCS/TR-1289}, institution = {Dept. Comp. Sci., Yale University}, keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems}, pubstate = {published}, tppubtype = {techreport} } |

Ferrari, S; Maggioni, Mauro; Borghese, N A Multi-Scale Approximation with Hierarchical Radial Basis Functions Networks, Journal Article IEEE Trans. on Neural Networks, 15 (1), pp. 178–188, 2004. BibTeX | Tags: approximation theory, multiscale analysis, radial basis functions @article{MRA_HRBF2004, title = {Multi-Scale Approximation with Hierarchical Radial Basis Functions Networks,}, author = {S Ferrari and Mauro Maggioni and N A Borghese}, year = {2004}, date = {2004-01-01}, journal = {IEEE Trans. on Neural Networks}, volume = {15}, number = {1}, pages = {178–188}, keywords = {approximation theory, multiscale analysis, radial basis functions}, pubstate = {published}, tppubtype = {article} } |

- Lectures at Summer School at Peking University, July 2017.
- PCMI Lectures, Summer 2016: Lecture 1, Lecture 2, Problems/discussion points
- Google Scholar
- Papers on the ArXiv
- Papers on MathsciNet
- Tutorials on diffusion geometry and multiscale analysis on graphs at the MRA Internet Program at IPAM: Part I and Part II.
- Diffusion Geometries, Diffusion Wavelets and Harmonic Analysis of large data sets, IPAM, Multiscale Geometric Analysis Program, Fall 2004.
- Diffusion Geometries, global and multiscale, IPAM, 2005.