Faculty

Mauro Maggioni

Bloomberg Distinguished Professor

Research Interests

Analysis, Partial Differential Equations, Algebraic Topology, Big Data, Data Intensive Computation, Harmonic Analysis over Manifolds and over Discrete Structures

Mauro Maggioni is a Bloomberg Distinguished Professor in the Department of Applied Mathematics and Statistics and in the Krieger School of Arts and Science’s Department of Mathematics. His research focuses on analysis, partial differential equations, algebraic topology, big data, data intensive computation, harmonic analysis manifolds and over discrete structures. He earned his doctorate in mathematics at Washington University in St. Louis (2002) and his bachelor’s and master’s at Università degli Studi di Milan (1999).

Education
  • Ph.D. 2002, Washington University in St. Louis
Research Areas
  • HARMONIC analysis (Mathematics)
  • Machine Learning trends
Journal Articles
  • Murphy JM, Maggioni M (2019).  Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion.  IEEE Transactions on Geoscience and Remote Sensing.  57(3).  1829-1845.
  • Vogelstein JT, Bridgeford EW, Wang Q, Priebe CE, Maggioni M, Shen C (2019).  Discovering and deciphering relationships across disparate data modalities.  eLife.  8.
  • Maggioni M, Lu F, Zhong M, Tang S (2018).  Nonparametric inference of interaction laws in systems of agents from trajectory data.
  • Maggioni M, Murphy M (2018).  Learning by unsupervised nonlinear diffusion.  Journal of Machine Learning Research.
  • Maggioni M, Little Anna, Murphy James M. (2018).  Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms.  Journal of Machine Learning Research.
  • Maggioni M, Vogelstein J, Bridgeford E, Tang M, Zheng D, Burns R (2018).  Geometric Dimensionality Reduction for Subsequent Classification.
  • Maggioni M, Escande P (2018).  Multiscale Decomposition of Transformations.
  • Maggioni M, Liao W, Vigogna S (2018).  Multiscale regression on unknown manifolds.  Journal of Machine Learning Research.
  • Maggioni M, Priebe C, Vogelstein J, Tomita T M, Patsolic J L, Browne J, Shen C, Yim J (2018).  Random Projection Forests.
  • Maggioni M, Lu F, Tang S (2018).  Learning governing laws in first order interacting agent systems: a Monte Carlo Approach.
  • Maggioni M, Vogelstein J, Priebe C, Shen C, Wang Q, Bridgeford E (2018).  Discovering and Deciphering Relationships Across Disparate Data Modalities.  eLife.
  • Maggioni M, Vigogna S, Lanteri A (2018).  Conditional Regression for Single-Index Models.
  • Maggioni M (2018).  Diffusion geometric methods for fusion of remotely sensed data.  Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral.  10644(International Society for Optics and Photonics).  106440I.
  • Maggioni M, Escande P, Weiss P, Debarnot V, Mangeat T (2018).  Learning and Exploiting Physics of Degradations.  Mathematics in Imaging, MTu2D.
  • Maggioni M (2018).  Diffusion geometric methods for fusion of remotely sensed data.  Proc. SPIE 10644,Algorithms and Technologies for Multispectral,Hyperspectral ....
  • Murphy JM, Maggioni M (2018).  Diffusion geometric methods for fusion of remotely sensed data.  Proceedings of SPIE - The International Society for Optical Engineering.  10644.
  • Escande P, Debarnot V, Maggioni M, Mangeat T, Weiss P (2018).  Learning and exploiting physics of degradations.  Optics InfoBase Conference Papers.  Part F105-MATH 2018.
  • Little AV, Maggioni M, Rosasco L (2017).  Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature.  Applied and Computational Harmonic Analysis.  43(3).  504-567.
  • Wang YG, Maggioni M, Chen G (2017).  Enhanced detection of chemical plumes in hyperspectral images and movies throughimproved backgroundmodeling.  Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing.  2015-June.
  • Gerber S, Maggioni M (2017).  Multiscale strategies for computing optimal transport.  Journal of Machine Learning Research.  18.  1-32.
  • Maggioni M, Vogelstein J, Tomita T (2017).  ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations.  Proceedings of the 2017 SIAM International Conference on Data Mining.
  • Bongini M, Fornasier M, Hansen M, Maggioni M (2017).  Inferring interaction rules from observations of evolutive systems I: The variational approach.  Mathematical Models and Methods in Applied Sciences.  27(5).  909-951.
  • Crosskey M, Maggioni M (2017).  ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds.  Multiscale Modeling and Simulation.  15(1).  110-156.
  • Tomita TM, Maggioni M, Vogelstein JT (2017).  ROFLMAO: Robust oblique forests with linear MAtrix operations.  Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017.  498-506.
  • Liao W, Maggioni M, Vigogna S (2016).  Learning adaptive multiscale approximations to data and functions near low-dimensional sets.  2016 IEEE Information Theory Workshop, ITW 2016.  226-230.
  • Goetzmann WN, Jones PW, Maggioni M, Walden J (2016).  Beauty is in the bid of the beholder: An empirical basis for style.  Research in Economics.  70(3).  388-402.
  • Wang Y, Chen G, Maggioni M (2016).  High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.  9(9).  4316-4324.
  • Yin R, Monson E, Honig E, Daubechies I, Maggioni M (2016).  Object recognition in art drawings: Transfer of a neural network.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2016-May.  2299-2303.
  • Maggioni M, Minsker S, Strawn N (2016).  Multiscale dictionary learning: Non-asymptotic bounds and robustness.  Journal of Machine Learning Research.  17.
  • Maggioni M, Minsker S, Strawn N (2015).  Geometric multi-resolution analysis for dictionary learning.  Proceedings of SPIE - The International Society for Optical Engineering.  9597.
  • Altemose N, Miga KH, Maggioni M, Willard HF (2014).  Genomic Characterization of Large Heterochromatic Gaps in the Human Genome Assembly.  PLoS Computational Biology.  10(5).
  • Gerber S, Maggioni M (2013).  Multiscale dictionaries, transforms, and learning in high-dimensions.  Proceedings of SPIE - The International Society for Optical Engineering.  8858.
  • Coppola A, Wenner BR, Ilkayeva O, Stevens RD, Maggioni M, Slotkin TA, Levin ED, Newgard CB (2013).  Branched-chain amino acids alter neurobehavioral function in rats.  American Journal of Physiology - Endocrinology and Metabolism.  304(4).
  • Krishnamurthy K, Mrozack A, Maggioni M, Brady D (2013).  Multiscale, dictionary-based speckle denoising.  Optics InfoBase Conference Papers.
  • Maggioni M (2013).  Geometric estimation of probability measures in high-dimensions.  Conference Record - Asilomar Conference on Signals, Systems and Computers.  1363-1367.
  • Bouvrie J, Maggioni M (2012).  Efficient solution of Markov decision problems with multiscale representations.  2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012.  474-481.
  • Bouvrie J, Maggioni M (2012).  Geometric multiscale reduction for autonomous and controlled nonlinear systems.  Proceedings of the IEEE Conference on Decision and Control.  4320-4327.
  • Chen G, Iwen M, Chin S, Maggioni M (2012).  A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements.  2012 IEEE Visual Communications and Image Processing, VCIP 2012.
  • Allard WK, Chen G, Maggioni M (2012).  Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis.  Applied and Computational Harmonic Analysis.  32(3).  435-462.
  • Zheng W, Rohrdanz MA, Maggioni M, Clementi C (2011).  Polymer reversal rate calculated via locally scaled diffusion map.  Journal of Chemical Physics.  134(14).
  • Rohrdanz MA, Zheng W, Maggioni M, Clementi C (2011).  Determination of reaction coordinates via locally scaled diffusion map.  Journal of Chemical Physics.  134(12).
  • Chen G, Maggioni M (2011).  Multiscale geometric and spectral analysis of plane arrangements.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.  2825-2832.
  • Monson EE, Chen G, Brady R, Maggioni M (2010).  Data representation and exploration with geometric wavelets.  VAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings.  243-244.
  • Jones PW, Maggioni M, Schul R (2010).  Universal local parametrizations via heat kernels and eigenfunctions of the laplacian.  Annales Academiae Scientiarum Fennicae Mathematica.  35(1).  131-174.
  • Willinger W, Rejaie R, Torkjazi M, Valafar M, Maggioni M (2010).  Research on online social networks: Time to face the real challenges.  Performance Evaluation Review.  37(3).  49-54.
  • Wu Q, Guinney J, Maggioni M, Mukherjee S (2010).  Learning gradients: Predictive models that infer geometry and statistical dependence.  Journal of Machine Learning Research.  11.  2175-2198.
  • Chen G, Maggioni M (2010).  Multiscale geometric wavelets for the analysis of point clouds.  2010 44th Annual Conference on Information Sciences and Systems, CISS 2010.
  • Little AV, Lee J, Jung YM, Maggioni M (2009).  Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD.  IEEE Workshop on Statistical Signal Processing Proceedings.  85-88.
  • Little AV, Jung YM, Maggioni M (2009).  Multiscale estimation of intrinsic dimensionality of data sets.  AAAI Fall Symposium - Technical Report.  FS-09-04.  26-33.
  • Mahoney MW, Maggioni M, Drineas P (2008).  Tensor-CUR decompositions for tensor-based data.  SIAM Journal on Matrix Analysis and Applications.  30(3).  957-987.
  • Coifman RR, Lafon S, Kevrekidis IG, Maggioni M, Nadler B (2008).  Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems.  Multiscale Modeling and Simulation.  7(2).  842-864.
  • Szlam AD, Maggioni M, Coifman RR (2008).  Regularization on graphs with function-adapted diffusion processes.  Journal of Machine Learning Research.  9.  1711-1739.
  • Maggioni M, Mhaskar HN (2008).  Diffusion polynomial frames on metric measure spaces.  Applied and Computational Harmonic Analysis.  24(3).  329-353.
  • Jones PW, Maggioni M, Schul R (2008).  Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels.  Proceedings of the National Academy of Sciences of the United States of America.  105(6).  1803-1808.
  • Mahadevan S, Maggioni M (2007).  Proto-value functions: A Laplacian framework for learning representation and control in Markov decision processes.  Journal of Machine Learning Research.  8.  2169-2231.
  • Prichep LS, Causevic E, Coifman RR, Isenhart R, Jacquin A, John ER, Maggioni M, Warner FJ (2006).  QEEG-based classification with wavelet packet and microstate features for triage applications in the ER.  ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  3.
  • Maggioni M, Mahadevan S (2006).  Fast direct policy evaluation using multiscale analysis of markov diffusion processes.  ACM International Conference Proceeding Series.  148.  601-608.
  • Mahadevan S, Maggioni M, Ferguson K, Osentoski S (2006).  Learning representation and control in continuous Markov decision processes.  Proceedings of the National Conference on Artificial Intelligence.  2.  1194-1199.
  • Mahoney MW, Maggioni M, Drineas P (2006).  Tensor-CUR decompositions for tensor-based data.  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.  2006.  327-336.
  • Maggioni M, Mahadevan S (2006).  Fast direct policy evaluation using multiscale analysis of Markov diffusion processes.  ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning.  2006.  601-608.
  • Coifman RR, Lafon S, Maggioni M, Keller Y, Szlam AD, Warner FJ, Zucker SW (2006).  Geometries of sensor outputs, inference and information processing.  Proceedings of SPIE - The International Society for Optical Engineering.  6232.
  • Bremer JC, Coifman RR, Maggioni M, Szlam AD (2006).  Diffusion wavelet packets.  Applied and Computational Harmonic Analysis.  21(1).  95-112.
  • Coifman RR, Maggioni M (2006).  Diffusion wavelets.  Applied and Computational Harmonic Analysis.  21(1).  53-94.
  • Maggioni M, Davis GL, Warner FJ, Geshwind FB, Coppi AC, DeVerse RA, Coifman RR (2006).  Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections.  Progress in Biomedical Optics and Imaging - Proceedings of SPIE.  6091.
  • Maggioni M, Bremer JC, Coifman RR, Szlam AD (2005).  Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs.  Proceedings of SPIE - The International Society for Optical Engineering.  5914.  1-13.
  • Szlam AD, Maggioni M, Coifman RR, Bremer JC (2005).  Diffusion-driven multiscale analysis on manifolds and graphs: Top-down and bottom-up constructions.  Proceedings of SPIE - The International Society for Optical Engineering.  5914.  1-11.
  • Mahadevan S, Maggioni M (2005).  Value function approximation with diffusion wavelets and Laplacian eigenfunctions.  Advances in Neural Information Processing Systems.  843-850.
  • Coifman RR, Maggioni M, Zucker SW, Kevrekidis IG (2005).  Geometric diffusions for the analysis of data from sensor networks.  Current Opinion in Neurobiology.  15(5).  576-584.
  • Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, Zucker SW (2005).  Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods.  Proceedings of the National Academy of Sciences of the United States of America.  102(21).  7432-7437.
  • Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, Zucker SW (2005).  Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps.  Proceedings of the National Academy of Sciences of the United States of America.  102(21).  7426-7431.
  • Maggioni M (2004).  Wavelet frames on groups and hypergroups via discretization of calderón formulas.  Monatshefte fur Mathematik.  143(4).  299-331.
  • Cassidy RJ, Berger J, Lee K, Maggioni M, Coifman RR (2004).  Analysis of hyperspectral colon tissue images using vocal synthesis models.  Conference Record - Asilomar Conference on Signals, Systems and Computers.  2.  1611-1615.
  • Ferrari S, Maggioni M, Borghese NA (2004).  Multiscale approximation with hierarchical radial basis functions networks.  IEEE Transactions on Neural Networks.  15(1).  178-188.
  • Chui CK, Czaja W, Maggioni M, Weiss G (2002).  Characterization of general tight wavelet frames with matrix dilations and tightness preserving oversampling.  Journal of Fourier Analysis and Applications.  8(2).  173-200.
  • Katz NH, Krop E, Maggioni M (2002).  Remarks on the box problem.  Mathematical Research Letters.  9(4).  515-519.
  • Maggioni M (2000).  M-Band Burt-Adelson Biorthogonal Wavelets.  Applied and Computational Harmonic Analysis.  9(3).  286-311.
  • Maggioni M (2000).  Critical exponent of short even filters and Burt-Adelson biorthogonal wavelets.  Monatshefte fur Mathematik.  131(1).  49-69.
Book Chapters
  • Chen G, Little AV, Maggioni M (2013).  Multi-resolution geometric analysis for data in high dimensions.  Excursions in Harmonic Analysis: The February Fourier Talks at the Norbert Wiener Center.  1.  259-285.
  • Chen G, Little AV, Maggioni M (2013).  Multi-resolution geometric analysis for data in high dimensions.  Applied and Numerical Harmonic Analysis.  (9780817683757).  259-285.
  • Chen G, Little AV, Maggioni M, Rosasco L (2011).  Some recent advances in multiscale geometric analysis of point clouds.  Applied and Numerical Harmonic Analysis.  (9780817680947).  199-225.
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