Ingrid Daubechies

Ingrid Daubechies
  • James B. Duke Distinguished Professor of Mathematics and Electrical and Computer Engineering
  • Professor in the Department of Mathematics
  • Professor in the Department of Electrical and Computer Engineering (Joint)

Research Areas and Keywords

Analysis
wavelets, inverse problems
Biological Modeling
shape space
Computational Mathematics
inverse problems
Geometry: Differential & Algebraic
shape space
Mathematical Physics
time-frequency analysis
Signals, Images & Data
wavelets, time-frequency analysis, art conservation
Education & Training
  • Ph.D., Vrije Universiteit Brussel (Belgium) 1980

Selected Grants

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022

Simons Foundation - Math + X Investigators awarded by Simons Foundation (Principal Investigator). 2016 to 2021

New Approaches for Better Spatial Frequency Localization in 2 and 3-Dimensional Data Analysis awarded by National Science Foundation (Principal Investigator). 2015 to 2020

Structured Dictionary Models and Learning for High Resolution Images awarded by National Science Foundation (Co-Principal Investigator). 2013 to 2017

Mathematics of Low Dimensional Represenations for Design awarded by Office of Naval Research (Co-Principal Investigator). 2011 to 2016

Discovery of Empirical Components by Information Theory, Random Matrix Theory, and Computational Topology awarded by Princeton University (Principal Investigator). 2013 to 2016

Voronin, S., and I. Daubechies. “An iteratively reweighted least squares algorithm for sparse regularization.” Contemporary Mathematics, vol. 693, 2017, pp. 391–411. Scopus, doi:10.1090/conm/693/13941. Full Text

Daubechies, I. Foreword. 2009, pp. xv–xvi.

Sabetsarvestani, Z., et al. “Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece..” Science Advances, vol. 5, no. 8, Aug. 2019. Epmc, doi:10.1126/sciadv.aaw7416. Full Text Open Access Copy

Alaifari, R., et al. “Stable Phase Retrieval in Infinite Dimensions.” Foundations of Computational Mathematics, vol. 19, no. 4, Aug. 2019, pp. 869–900. Scopus, doi:10.1007/s10208-018-9399-7. Full Text

Shan, S., et al. “ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal.” Methods in Ecology and Evolution, vol. 10, no. 4, Apr. 2019, pp. 541–52. Scopus, doi:10.1111/2041-210X.13148. Full Text

Zhu, W., et al. “LDMNet: Low Dimensional Manifold Regularized Neural Networks.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2018, pp. 2743–51. Scopus, doi:10.1109/CVPR.2018.00290. Full Text

Yin, R., and I. Daubechies. “Directional Wavelet Bases Constructions with Dyadic Quincunx Subsampling.” Journal of Fourier Analysis and Applications, vol. 24, no. 3, June 2018, pp. 872–907. Scopus, doi:10.1007/s00041-017-9540-z. Full Text

Gao, Tingran, et al. “Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation..” Anatomical Record (Hoboken, N.J. : 2007), vol. 301, no. 4, Apr. 2018, pp. 636–58. Epmc, doi:10.1002/ar.23700. Full Text Open Access Copy

Xu, J., et al. “Recursive diffeomorphism-based regression for shape functions.” Siam Journal on Mathematical Analysis, vol. 50, no. 1, Jan. 2018, pp. 5–32. Scopus, doi:10.1137/16M1097535. Full Text Open Access Copy

Alaifari, R., et al. “Reconstructing Real-Valued Functions from Unsigned Coefficients with Respect to Wavelet and Other Frames.” Journal of Fourier Analysis and Applications, vol. 23, no. 6, Dec. 2017, pp. 1480–94. Scopus, doi:10.1007/s00041-016-9513-7. Full Text

Fodor, G., et al. “Cradle removal in X-ray images of panel paintings.” Image Processing on Line, vol. 7, Jan. 2017, pp. 23–42. Scopus, doi:10.5201/ipol.2017.174. Full Text

Cornelis, Bruno, et al. “Removal of Canvas Patterns in Digital Acquisitions of Paintings..” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society, vol. 26, no. 1, Jan. 2017, pp. 160–71. Epmc, doi:10.1109/tip.2016.2621413. Full Text Open Access Copy

Pages

Deligiannis, Nikos, et al. “Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation..” Ieee Transactions on Image Processing : A Publication of the Ieee Signal Processing Society, vol. 26, no. 2, 2017, pp. 751–64. Epmc, doi:10.1109/tip.2016.2623484. Full Text

Deligiannis, N., et al. “X-ray image separation via coupled dictionary learning.” Proceedings  International Conference on Image Processing, Icip, vol. 2016-August, 2016, pp. 3533–37. Scopus, doi:10.1109/ICIP.2016.7533017. Full Text

Yin, R., et al. “Object recognition in art drawings: Transfer of a neural network.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 2016-May, 2016, pp. 2299–303. Scopus, doi:10.1109/ICASSP.2016.7472087. Full Text

Yin, R., et al. “Digital cradle removal in X-ray images of art paintings.” 2014 Ieee International Conference on Image Processing, Icip 2014, 2014, pp. 4299–303. Scopus, doi:10.1109/ICIP.2014.7025873. Full Text

Puente, Jesus, et al. “Automated approaches to geometric morphometrics..” American Journal of Physical Anthropology, vol. 150, WILEY-BLACKWELL, 2013, pp. 226–226.

Rudin, C., et al. “On the dynamics of boosting.” Advances in Neural Information Processing Systems, 2004.

Cvetković, Z., et al. “Interpolation of bandlimited functions from quantized irregular samples.” Data Compression Conference Proceedings, vol. 2002-January, 2002, pp. 412–21. Scopus, doi:10.1109/DCC.2002.999981. Full Text

Moayeri, N., et al. “Wavelet transform image coding using trellis coded vector quantization.” Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, vol. 4, 1992, pp. 405–08. Scopus, doi:10.1109/ICASSP.1992.226350. Full Text

Ingrid Daubechies

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Ingrid Daubechies

Duke Mathematics professor Ingrid Daubechies was one of nine to be awarded an honorary degree from Harvard University. One of the world’s leading mathematicians, a member of the National Academy of Sciences and the National Academy of Engineering, a... read more »


Ingrid Daubechies

Duke Math Faculty Ingrid Daubechies was interviewed by Forbes Magazine, resulting in the article "... read more »


Women in Science - UNESCO

Ingrid Daubechies is one of five outstanding women scientists from different parts of the world recognized for their excellence in the fields of material science, mathematics and computer science.  They will each receive € 100,000 and will be... read more »


Ingrid Daubechies

The 2018 Fudan-Zhongzhi Science Award has been awarded to Ingrid Daubechies, James B. Duke Professor of Mathematics and Electrical and... read more »


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