特别副研究员

当前位置: Jean-luc

Jean-luc

个人简介


Personal Information

Name:  Jean-Luc Bouchot

Born:  1986, France

Email:  jlbouchot@bit.edu.cn 

Institute:  School of Mathematics and Statistics, BIT, 102400, China

Address: No.5 Zhongguancun South Street, Haidian District, Beijing, China


Educational Background

► 10/2009--10/2012, PhD in Mathematical Methods in Engineering, Department of Knowledg-based Mathematical Systems, Johannes Kepler University, Austria. Supervisor: Prof. E. Peter Klement.

► 09/2006--09/2009, Master in Engineering in Applied Mathematics and Computer Science, National Polytechnic Institute of Toulouse (INP-ENSEEIHT), France 


Working Experience

► 10/2018—Present,Assistant Professor, School of Mathematics and Statistics, Beijing Institute of Technology

► 09/2014--10/2018, Postdoctoral researcher, Chair C for Mathematics (Analysis), RWTH Aachen University, Germany. Supervisor: Prof. Holger Rauhut

► 11/2012–08/2014, Visiting Assistant Professor in Mathematics, Department of Mathematics, Drexel University, Philadelphia, USA.

► 10/2009--09/2012, Research assistant, Department of Knowledge-based Mathematical Systems, Johannes Kepler University, Austria.

► 06/2010--09/2011, Project Assistant, Christian Doppler Laboratory MS-MACH, Johannes Kepler University, Austria.

► 08/2008--09/2009, Smart embedded software developer, T-Systems International GmbH, Darmstadt, Germany


Teachings

► Summer 2019: Guest lecturer,  Compressend sensing and applications, Academia Sinica, Taipei, Taiwan, Republic of China

► 2018/2019: Matrix analys, Beijing Institute of Technology

► 2014-2018: Higher Mathematics I,II,III, RWTH Aachen University

► 2018: Mathematical and computational genomic, RWTH Aachen University

► 2016: Mathematical Foundations of Machine Learning, RWTH Aachen University

► 2014: Numerical Analysis I & II, Drexel University

► 2013: Numerical Analysis I

► 2013: Differential Equations for Engineers, Drexel University

► 2013: Introductory analysis, Drexel University


Publications

(updated list, including preprints, available at https://scholar.google.com/citations?hl=en&user=sXMPbkMAAAAJ&view_op=list_works&sortby=pubdate or https://jlbouchot.github.io/publication.html )

[1] B. Bah, J.-L. Bouchot: Recent developments in signal approximation and reconstruction, Frontiers in Applied Mathematics and Statistics, March 2020.

[2] R. Aceska, J.-L. Bouchot, S. Li: Local sparsity and recovery of fusion frame structured signals, Signal Processing 174, 2020

[3] J.-L. Bouchot, K. Hamm: Stability and robustness of RBF interpolation, Sampling theory in signal and image processing 16, 37-53, 2017.

[4] R. Aceska, J.-L. Bouchot, S. Li: Fusion frames and distributed sparsity, Contemporary Mathematics 706, 47-62, 2016.

[5] J.-L. Bouchot, S. Foucart, P. Hitczenko: Hard thresholding pursuit algorithms: Number of iterations, Applied and Computational Harmonic Analysis 41(2), 412-435, 2016.

[6] J.-L. Bouchot, B. Bykowski, H. Rauhut, Ch. Schwab: Compressed sensing Petrov-Galerkin approximations for parametric PDEs, 2015 Internation conference on Sampling Theory and Applications (SampTA), 528-532, 2015.

[7] J.-L. Bouchot, L. Cao: Numerical solution of underdetermined systems from partial circulant measurements, 2015 International conference on Sampling Theory and Applications (SampTA), 264-268, 2015.

[8] J.-L. Bouchot, F. Bauer: Discrepancy norm: Approximation and variations, Journal of Computational and Applied Mathematics 272, 162-179, 2014.

[9] J.-L. Bouchot, F. Morain-Nicolier: Scaled-distance-transform and monotonicity of autocorrelations, IEEE Signal Processing Letters 21(10), 1235-1239, 2014.

[10] J.-L. Bouchot: A generalized class of hard thresholding algorithms for sparse signal recovery, Approximation Theory XIV: San Antonio 2013, Springer Proceedings in Mathematics and Statistics 83, 2014.

[11] J.-L. Bouchot, W. L. Trimble, G. Ditzler, Y. Lan, S. Essinger, G. Rosen: Advances in machine learning for processing and comparison of metagenomic data, Computational Systems Biology (2nd Edition), 295-329, 2014.

[12] G. Ditzler, Y. Lan, J.-L. Bouchot, G. Rosen: Variable selection to improve classification of metagenomes, in Encyclopedia of Metagenomics, Springer, 2013.

[13] S. Bernstein, J.-L. Bouchot, M. Reinhardt, B. Heise: Generalized analytic signals in image processing: Comparison, theory, and applications, Quaternion and Clifford Fourier Transforms and Wavelets in Trends in Mathematics, 221-246, 2013.

[14] E. Leiss-Holzinger, U. Cakmak, B. Heise, J.-L. Bouchot, E. Klement, M. Leitner, D. Stifter, Z. Major: Evaluation of structural change and local strain distribution in polymers comparatively imaged by FFSA and OCT techniques, Express Polymer Letters, 6(3), 249-256, 2012.

[15] G. Stübl, J.-L. Bouchot, P. Haslinger, B. Moser: Discrepancy norm as fitness for defect for defect detection on regularly textured surfaces, DAGM/OAGM 2012: Pattern Recognition, 428-437, 2012.

[16] E. Lughofer, J.-L. Bouchot, A. Shaker: On-Line Elimination of Local Redundancies in Evolving Fuzzy Systems, Evolving Systems, 2(3), 165-187, 2011.

[17] D. Stifter, E. Leiss-Holzinger, Heise, J.-L. Bouchot, Z. Major, M., Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger: Spectral domain polarization sensitive optical coherence tomography at 1.55 µm: novel developments and applications for dynamic studies in materials science, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XV, Proceedings of SPIE Vol. 7889 (SPIE, Bellingham, WA 2011) 78890Z, 2011

[18] J.-L. Bouchot, G. Stübl, B. Moser: A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces, Proceedings Volume 8000, Tenth International Conference on Quality Control by Artificial Vision; 80000K, 2011.

[19] B. Moser, G. Stübl and J.-L. Bouchot: On a Non-Monotonicity Effect of Similarity Measures, Similarity-Based Pattern Recognition (SIMBAD 2011), 46-60, Springer, 2011.

[20] J.-L. Bouchot, J. Himmelbauer, B. Moser: On autocorrelation based on Hermann Weyl's discrepancy norm for time series analysis, The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 1-7, 2010.


Visiting Positions

► 06/2019 (1 week), Visiting Jun Yu in Department of Statistics, University of Umea, Sweden.

► 04/2019 (1 week), Visiting Song Li in the Department of data science, ZheJiang University (HangZhou), PR. China

► 05/2015–06/2015, Visiting researcher, CRM, Barcelona, Spain, as part of the IRP on Approximation and Harmonic Analysis

► 01/2015--04/2015, Visiting Researcher, HIM, Bonn, Germany, as part of the program: Mathematics of signal processing.


Conference Talks and Invited Presentations

(selected talks in the last 3 years)

►06/2019, Uncertainty quantification via compressed sensing, Department of Statistics, Umea University, Sweden

► 06/2019: Compressed sensing and high-dimensional parametric PDEs, workshop on High-dimensional data analysis, Uppsala, Sweden

►04/2019: Compressed sensing and uncertainty quantification, Data science institute, Zhejiang University, P.R.China

►04/2018, Sampling and reconstruction of high-dimensional phenomena, Centre for Data Science, New York University, USA

►04/2018: Compressed sensing, distributed networks, and fusion frames; South African Numerical and Applied Mathematics conference, Stellenbosch, South Africa

►02/2018, Uncertainty quantification in parametric PDEs, MAP5, Diderot University, Paris, France

►11/2017, Compressed sensing: Novel results and applications, Department of Mathematics, University of Victoria, Wellington, New Zealand

►10/2017, Parametric function approximation in Hilbert spaces, SPOT seminar, National Polytechnic Institute, Toulouse, France


Interests

My research is in the area of mathematical signal and data processing with a strong emphasis on computational aspects. I worked on algorithms for sparse signal recovery and apply these techniques to engineering problems coming from sensor clusters, uncertainty quantification in applied mathematics, genomic data.