Distinguished Professor, Vice Dean
Department of Mathematics
Harbin Institute of Technology
Harbin 150001, China
Chinese Homepage
Contact
Email: jma@hit.edu.cn
NEWS
SPOC 2013 (Int. Conf. Signal Processing, Optimization and Compressed Sensing), Dec. 27-31, **-08-20
The 2nd Guangzhou International Workshop on Mathematical Imaging, Dec. 14-15, **-10-21
The First International Workshop on Mathematical Geophysics, January 8-11, **-03-08
1970-01-01
8th International Congress on Industrial and Applied Mathematics (ICIAM 2015)2014-09-04
Madagascar school at Harbin, Jan 7-8, **-11-09
HIT International Summer School on Pure and Applied Mathematics, July 6- Aug. 9, **-02-23
SEG Geophysical Compressed Sensing Workshop, December 2-4, **-09-08
Computational Seismology Workshop, Tsinghua Sanya International Mathematics Forum，Jan. 4-8, **-12-19
HONOR
National Talents of Outstanding Youth
Longjiang Chair Professor
Chinese Top 100 Most Impact Papers Published in International Journals in 2010
New Century Excellent Talents in University (by Ministry of Education of China, 2011)
Cheng-Yi Fu Award (by Chinese Geophysical Society, 2011)
EDUCATION
1998 - 2002 Ph. D. Solid Mechanics, Tsinghua University1994 - 1998 B. S. Engineering Mechanics, Dalian University of Technology
WORK EXPERIENCE
2011 - now
Professor
Harbin Institute of Technology, China
2010 - 2011
Scientist
Florida State University, USA
2006 - 2010
Assistant Professor/Associate Professor
Tsinghua University, China
2002 - 2006
Postdoc
University of Cambridge, UK
University of Grenoble I, France, etc.
2014.3-2014.9
University of Texas at Austin, USA
2013.7-2013.8
Hongkong Baptist University, HK
2012.12.3-10
National University of Singapore
2009.3-2009.9
Ecole des Mines de Paris, France
2008.5-2008.6
University of Grenoble I, France
2007.6-2007.9
Florida State University, USA
2006.6-2006.8
EPFL, Switzerland
PROFESSIONAL SOCIETIES
IEEE Senior Member, SEG Member
Committee member for China Society for Industrial and Applied Mathematics (China SIAM)
Technical Committee member for IEEE Instrumentation and Measurement Society
Reviewer for NSFC, US-Israel NSF, CSC
Selected Research Grants
NSFC (Mathmatical Division) on Low-rank matrix completion and seismic applications, 2014-2016
NSFC (Geoscience Division) on 3D data-driven tight frame for seismic data reconstruction, 2014-2017
NSFC (Information Division) on Dynamic configurable imaging system based on compressed sensing, 2014-2017
NSFC (Geoscience Division) on Curvelets for seismic wave equations, 2008-2010
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Jianwei Ma
Distinguished Professor, Vice Dean
Department of Mathematics
Harbin Institute of Technology
Harbin 150001, China
Chinese Homepage
Contact
Email: jma@hit.edu.cn
NEWS
SPOC 2013 (Int. Conf. Signal Processing, Optimization and Compressed Sensing), Dec. 27-31, **-08-20
The 2nd Guangzhou International Workshop on Mathematical Imaging, Dec. 14-15, **-10-21
The First International Workshop on Mathematical Geophysics, January 8-11, **-03-08
1970-01-01
8th International Congress on Industrial and Applied Mathematics (ICIAM 2015)2014-09-04
Madagascar school at Harbin, Jan 7-8, **-11-09
HIT International Summer School on Pure and Applied Mathematics, July 6- Aug. 9, **-02-23
SEG Geophysical Compressed Sensing Workshop, December 2-4, **-09-08
Computational Seismology Workshop, Tsinghua Sanya International Mathematics Forum，Jan. 4-8, **-12-19
HONOR
National Talents of Outstanding Youth
Longjiang Chair Professor
Chinese Top 100 Most Impact Papers Published in International Journals in 2010
New Century Excellent Talents in University (by Ministry of Education of China, 2011)
Cheng-Yi Fu Award (by Chinese Geophysical Society, 2011)
EDUCATION
1998 - 2002 Ph. D. Solid Mechanics, Tsinghua University1994 - 1998 B. S. Engineering Mechanics, Dalian University of Technology
WORK EXPERIENCE
2011 - now
Professor
Harbin Institute of Technology, China
2010 - 2011
Scientist
Florida State University, USA
2006 - 2010
Assistant Professor/Associate Professor
Tsinghua University, China
2002 - 2006
Postdoc
University of Cambridge, UK
University of Grenoble I, France, etc.
2014.3-2014.9
University of Texas at Austin, USA
2013.7-2013.8
Hongkong Baptist University, HK
2012.12.3-10
National University of Singapore
2009.3-2009.9
Ecole des Mines de Paris, France
2008.5-2008.6
University of Grenoble I, France
2007.6-2007.9
Florida State University, USA
2006.6-2006.8
EPFL, Switzerland
PROFESSIONAL SOCIETIES
IEEE Senior Member, SEG Member
Committee member for China Society for Industrial and Applied Mathematics (China SIAM)
Technical Committee member for IEEE Instrumentation and Measurement Society
Reviewer for NSFC, US-Israel NSF, CSC
Selected Research Grants
NSFC (Mathmatical Division) on Low-rank matrix completion and seismic applications, 2014-2016
NSFC (Geoscience Division) on 3D data-driven tight frame for seismic data reconstruction, 2014-2017
NSFC (Information Division) on Dynamic configurable imaging system based on compressed sensing, 2014-2017
NSFC (Geoscience Division) on Curvelets for seismic wave equations, 2008-2010
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RESEARCH INTERESTS
Seismic ExplorationCompressed SensingSparse TansformsImage ProcessingRemote Sensing
COLLABORATORS
Prof. Anestis Antoniadis University of Joseph Fourier, Grenoble, France
Dr. Florian Bossmann University of Goettingen, Germany
Mr. Simons Bechouche CEA, France
Prof. Herve Chauris Ecole des Mines de Paris, France
Prof. Francois Le Dimet University of Joseph Fourier and INRIA, France
Prof. Sergey Fomel University of Texas at Austin, USA
Prof. Yue M Lu Harvard University, USA
Prof. Gorden Erlebacher Florida State University, USA
Dr. Markus Fenn University of Mannheim, Germany
Prof. Felix Herrmann University of British Columbia, Canada
Prof. M Yousuff Hussaini Florida State University, USA
Prof. Jane Jiang University of Huddersfield, UK
Dr. Jens Krommweh University of Duisburg-Essen, Germany
Prof. Stanley Osher University of California at Los Angeles, USA
Prof. Gerlind Plonka University of Goettingen, Germany
Prof. Mauricio Sacchi University of Alberta, Canada
Prof. Gabriele Steidl University of Kaiserslautern, Germany
Prof. Oleg Vasilyev University of Colorado at Boulder, USA
Prof. JohnVillasenor University of California at Los Angeles, USA
Prof. Jiangtao Wen Tsinghua University (Computer Science), China
Prof. Huizhu Yang Tsinghua University (School of Aeropace), China
Prof. Datian Ye Tsinghua University (Biomedical Engineering), China
Prof. Wotao Yin Rice University, USA
Prof. Xiaoqun Zhang Shanghai Jiao Tong University, China
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Publications
Google Scholar Citations
Google Scholar Citations
INVITED BOOK CHAPTER
2. J. Ma, A. S. Khwaja, M. Y. Hussaini, Compressed remote sensing, in Signal and Image Processing for Remote Sensing (Editor by Chi H. Chen), the second version, 2012, 73-90.
1. G. Plonka, J. Ma, Curvelets, in Encyclopedia of Applied and Computational Mathematics (Editor by B. Engquist), Springer Berlin, 2013.
JOURNAL ARTICLES (* corresponding author)
76. S. Yu, J. Ma, S. Osher, Geometrical Mode Decomposition, 2016.
75. S. Yu, J. Ma, et al., De-aliased reconstruction of seismic records by polar Fourier transform, 2016.
74. L. Li, F. Le Dimet, J. Ma, et al., Level-set based variational data assimilation of oil spill dispersion, 2016.
73. H. Wang, M. Sacchi, J. Ma*, Linearized dynamic warping with L1-norm constraint for multi-component registration, 2016, Journal of Applied Geophysics, submitted.
72. L. Liu, J. Ma*, J.-F. Cai, Data-driven Kronecker tight frame and applications in seismic data interpolation and denoising, 2016, Geophysics, submitted.
71. F. Bossmann, J. Ma*, Asymmetric chirplet transform II: phase, frequency, and chirp rate, 2016, Geophysics, revised.
70. S. Yu, J. Ma*, S. Osher, Variational model decomposition of seismic data, 2016, Geophysics, revised.
69. C. Zhang, B. Sun, H. Yang, J. Ma, A non-split perfectly matched layer absorbing boundary condition for the second-order wave equation modeling, 2016, Journal of Seismic Exploration, revised.
68. C. Zhang, B. Sun, J. Ma, H. Yang, Splitting algorithm for high-order compact finite diference scheme in wave equation modeling, Geophysics, 2016, revised.
67. J. Fitschen*, J. Ma, S. Schuff, Removel of curtaining effects by a variational model with directional first and second order differences, 2016, IEEE Trans. Image Processing, revised.
66. S. Yu, J. Ma*, S. Osher, Monte Carlo data-driven tight frame for seismic data recovery, Geophysics, 2016, accepted.
65. Y. Chen, J. Ma*, S. Fomel, Double sparsity dictionary for seismic noise attenuation, Geophysics, 2016, 81 (2), V17-V30.
64. F. Bossmann, J. Ma*, Asymmetric chirplet transform for sparse representation of seismic data, Geophysics, 2015, 80 (6), WD89-WD100.
63. S. Yu, J. Ma*, X. Zhang, M. Sacchi, Denoising and interpolation of high-dimensional seismic data by learning a tight frame, Geophysics, 2015, 80 (5), V119-V132.
62. Y. Chen*, S. Jiao, J. Ma, et al., Ground-roll noise attenuation using a simple and effective approach based on local bandlimited orthogonalization, IEEE Geoscience and Remote Sensing Letters, 2015, 12 (11), 2316-2320.
61. G. Tang, W. Hou, H. Wang, G. Luo, J. Ma, Compressed sensing of roller bearing faults via harmonic detection from under-sampled vibration signals, Sensors, 2015, 15 (10), 25648-25662,
60. G. Tang, Q. Yang, H. Wang*, G. Luo, J. Ma, Sparse classfication of rotating machinery faults based on compressive sensing, Mechatronics, 2015, 31, 60-67.
59. J. Wang, J. Ma*, B. Han, Y. Chen, Seismic data reconstruction via weighted nuclear-norm minimization, Inverse Problem in Science and Engineering, 2015, 23, 2, 277–291.
58. S. Beckouche, J. Ma*, Simultaneously dictionary learning and denoising for seismic data, Geophysics, 2014, 79 (3), A27-A31.
57. J. Liang, J. Ma*, X. Zhang, Seismic data restoration via data-driven tight frame, Geophysics, 2014, 79 (3), V65-V74.
56. H. Wang, Y. Chen, J. Ma*, Curvelet-based registration of multi-component seismic waves, Journal of Applied Geophysics, 2014, 104, 90-96.
55. Y. Yang, J. Ma, S. Osher, Seismic data reconstruction via matrix completion, Inverse Problem and Imaging, 2013, 7 (4), 1379-1392.
54. J. Ma*, Three-dimensional irregular seismic data reconstruction via low-rank matrix completion, Geophysics, 2013, 78 (5), V181-V192.
53. R. Shahidi*, G. Tang, J. Ma, F. Herrmann, Application of randomized sampling schemes to curvelet-based sparsity-promoting seismic data recovery, Geophysical Prospecting, 2013, 61 (5), 973-997.
52. Q. Li, J. Ma*, G. Erlebacher, A new reweighted algorithm with support detection for compressed sensing, IEEE Signal Processing Letters, 2012, 19 (7), 419-422.
51. Y. He, M. Y. Hussaini, J. Ma*, B. Shafei, G. Steidl, A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data, Pattern Recognition, 2012, 45, 3463-3471.
50. J. Ma*, G. Plonka, M. Y. Hussaini, Compressive video sampling with approximate message passing decoding, IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22 (9), 1354-1364.
49. J. Wang, J. Ma*, B. Han, Qin Li, Split Bregman iterative algorithm for sparse reconstruction of electrical impedance tomography, Signal Processing, 2012, 92, 2952-2961.
48. J. Xu, J. Ma*, Y. Zhang et al., Improved total variation minimization method for compressive sensing by intra prediction, Signal Processing, 2012, 92, 2614-2623.
47. K. Tsai, J. Ma*, D. Ye, J. Wu, Curvelet processing of MRI for local image enhancement, International Journal for Numerical Methods in Biomedical Engineering, 2012, 28 (6-7), 661-677.
46. W. Shi, A. S. Khwaja, J. Ma*, Compressed sensing of complex-value data, Signal Processing, 2012, 92, 357–362.
45. H. Yu*, L. Wu, L. Guo, J. Ma, H. Li, A domain-independent interaction integral for fracture analysis of nonhomogeneous piezoelectric materials, Int. J. Solids and Structures, 2012,49, 3301-3315.
44. G. Tang, J. Ma, H. Yang, Seismic denoising via learning sparse dictionary, Applied Geophysics, 2012, 9, 27-32.
43. J. Ma*, M. Y. Hussaini, Extensions of compressed imaging: flying sensor, coded mask, and fast decoding, IEEE Transactions on Instrumentation and Measurement, 2011, 60 (9), 3128-3139.
42. A. S. Khwaja, J. Ma*, Applications of compressed sensing for SAR moving target velocity estimation and image compression, IEEE Transactions on Instrumentation and Measurement, 60 (8), 2828-2860, 2011.
41. B. Sun, H. Chauris*, J. Ma, 3D post-stack one-way migration using curvelets, Journal of Seismic Exploration, 20 (3), 2011.
40. G. Plonka*, J. Ma, Curvelet-wavelet regularized split Bregman iteration for compressed sensing, Int. J. Wavelet, Multiresolution Information Processing, 2011, 9 (1), 79-110.
39. J. Ma*, Improved iterative curvelet thresholding for compressed sensing, IEEE Transactions on Instrumentation and Measurement, 2011, 60 (1), 126-136.
38. J. Ma*, Compressed sensing by iterative thresholding of geometric wavelets: a comparing study, Int. J. Wavelet, Multiresolution Information Processing, 2011, 9, 63-77
37. G. Tang, J. Ma*, Applications of total variation based curvelet shrinkage for three-dimensional seismic denoising, IEEE Geoscience and Remote Sensing Letters, 2011, 8 (1), 103-107.
36. J. Ma*, Compressed sensing for surface characterization and metrology, IEEE Transactions on Instrumentation and Measurement, 2010, 59 (6), 1600-1615.
35. J. Ma*, G. Plonka, The curvelet transform, IEEE Signal Processing Magazine, 2010, 27 (2), 118-133.
34. J. Krommweh, J. Ma*, Tetrolet shrinkage with anisotropic TV minimization for image approximation, Signal Processing, 2010, 90, 2529-2539.
33. J. Ma*, G. Plonka, H. Chauris, A new sparse representation of seismic data using adaptive easy-path wavelet transform, IEEE Geoscience and Remote Sensing Letters, 2010, 7 (3), 540-544.
32. H. Shan, J. Ma*, Curvelet-based geodesic active contours for multiple objects image segmentation, Pattern Recognition Letters, 2010, 31 (5), 355-360.
31. T. Mi, J. Ma, H. Chauris*, H. Yang, Multilevel adaptive mesh modeling for wave propagation in layer media, Journal of Seismic Exploration, 2010, 19 (2), 121-139.
30. J. Liu, J. Ma, H. Yang, Research on P-wave’s propagation in White’s sphere model with patch saturation, Chinese J. Geophysics, 2010, 53, 954-962.
29. J. Liu, J. Ba, J. Ma, H. Yang, An analysis of seismic attenuation in random porous media, Science in China, Series G, 2010, 53, 628-637.
28. Y. Tian, J. Ma, H. Yang, Wave field simulation for a porous medium saturated by two immiscible fluids, Applied Geophysics, 2010, 7: 57-65.
27. J. Ma*, F.-X. Le Dimet, Deblurring from highly incomplete measurements for remote sensing, IEEE Transactions on Geoscience and Remote Sensing, 2009, 47 (3), 792-802.
26. J. Ma*, A single-pixel imaging system for remote sensing using two-step iterative curvelet thresholding, IEEE Geoscience and Remote Sensing Letters, 2009, 6 (4), 676-680.
25. J. Ma*, Single-pixel remote sensing, IEEE Geoscience and Remote Sensing Letters, 2009, 6 (2), 199-203.
24. J. Ma, M. Y. Hussaini*, O. Vasilyev, F. Le Dimet, Multiscale Geometric analysis of turbulence by curvelets, Physics of Fluids, 2009, 21, 075104.
23. H. Shan, J. Ma*, H. Yang, Comparisons of wavelets, contourlets and curvelets for seismic denoising, Journal of Applied Geophysics, 2009, 69, 103-115.
22. B. Sun, J. Ma, H. Chauris*, H. Yang, Solving the wave equation in the curvelet domain: a mulit-scale and multi-directional approach, Journal of Seismic Exploration, 2009, 18, 385-399.
21. Y. Tian, Z. Zhang, J. Ma, H. Yang, Inversing physical parameter of saturated porous viscoelastic media by homotopy method, Chinese J. Geophysics, 2009, 52, 2328-2334.
20. J. Liu, J. Ma, H. Yang, Research on dispersion and attenuation of P wave in periodic layered-model with patchy saturation, Chinese J. Geophysics, 2009, 52, 2879-2885.
19. J. Liu, J. Ma, H. Yang, The study of perfectly matched layer absorbing boundaries for SH wave fields, Applied Geophysics, 2009, 6, 267-274.
18. J. Ma*, Compressed sensing by inverse scale space and curvelet thresholding, Applied Mathematics and Computation, 2008, 206, 980-988.
17. G. Plonka*, J. Ma, Nonlinear regularized reaction-diffusion filters for denoising of images with textures, IEEE Transactions on Image Processing, 2008, 17 (8), 1283-1294.
16. X. Jiang, W. Zeng, P. Scott, J. Ma, L. Blunt, Linear feature extraction based on complex ridgelet transform, Wear, 2008, 265, 428-433.
15. J. Ma*, G. Plonka, Combined curvelet shrinkage and nonlinear anisotropic diffusion, IEEE Transactions on Image Processing, 2007, 16 (9), 2198-2206.
14. J. Ma, M. Y. Hussaini*, There-dimensional curvelets for coherent vortex analysis of turbulence, Applied Physics Letters, 2007, 91, 184101.
13. J. Ma*, Curvelets for surface characterization, Applied Physics Letters, 2007, 90, 054109.
12. J. Ma*, Characterization of textual surfaces using wave atoms, Applied Physics Letters, 2007, 90, 264101.
11. J. Ma*, Deblurring using singular integrals and curvelet shrinkage, Physics Letters A, 2007, 368, 245-250.
10. G. Plonka*, J. Ma, Convergence of an iterative nonlinear scheme for denoising of piecewise constant images, Int. J. Wavelet, Multiresolution and Information Processing, 2007, 5, 975-995.
9. J. Ma*, M. Fenn, Combined complex ridgelet shrinkage and total variation minimization, SIAM Journal on Scientific Computing, 2006, 28 (3), 984-1000.
8. J. Ma*, A. Antoniadis, F.-X. Le Dimet, Curvelets-based multiscale detection and tracking for geophysical fluids, IEEE Transactions on Geoscience and Remote Sensing, 2006, 44 (12), 3626-3638.
7. J. Ma*, Towards artifact-free characterization of surface topography using complex wavelets transform and total variation minimization, Appl. Math. Comput., 2005, 270, 1014-1030.
6. J. Ma*, J. Xiang, P. Scott, Complex ridgelets for shift invariant characterization of surface topography with line singularities, Physics Letters A, 2005, 344, 423-431.
5. J. Ma*, An exploration of multiresolution symplectic scheme for wave propagation using second generation wavelets, Physics Letters A, 2004, 328 (1), 36-46.
4. J. Ma, H. Yang, Multiresolution symplectic scheme for wave propagation in complex media, Appl. Math. Mech.-ENGL, 2004, 25 (5), 523-528.
3. J. Ma*, H. Yang, Y. Zhu, MRFD method for numerical solution of wave propagation in layered media with general boundary condition, IEE Electronics Letters, 2001, 37 (20), 1267-1268.
2. J. Ma*, Y. Zhu, H. Yang, Multiscale-combined seismic waveform inversion using orthogonal wavelet transform, IEE Electronics Letters, 2001, 37 (4), 261-262.
1. J. Ma*, H. Yang, Simulation of acoustic wave propagation in complex media using MRFD method, Acat Phys. Sin.-CH ED, 2001, 50 (8), 1415-1420.
PREPRINTS
7. Y. Jia, S. Yu, L. Liu, J. Ma, Orthogonal rank-one matrix pursuit for 3D seismic data interpolation, 2015.
6. C. Bao, J. Ma, H. Ji, Two-domain regularization for the seismic data interpolation, 2014.
5. J. Ma, Fast low patch-rank method for interpolation of regularly missing traces, 2014.
4. J. Ma, Y. Yang, S. Osher, J. Gilles, Image reconstruction in compressed remote sensing with low-rank and L1-norm regularization, 2012.
3. S. Hauser, J. Ma, Seismic data reconstruction via Shearlet-regularized directional inpainting, 2012.
2. Q. Li, G. Erlebacher, J. Ma, Reweighted alternating direction method of multiplier for non-convex compressed sensing, 2011.
1. W. Tan, J. Ma, F. Herrmann, Improved compressed sensing for seismic data regularization, online in Rice-CS homepage, 2009.
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Team Members
Assistant Professor:
Chang Yang (Ph.D., University Lille I-Science et Technology, 2011), Scientific Computing
Xiaoran Shi (Ph.D., University of Science and Technology, 2012), Geometric and Image Processing
Hongjun Yu (Ph.D., Harbin Institute of Technology, 2010), Solid Mechanics
Ph.D. Students:
Hairong Wang 2012- , Multi-component wave registration (Visit Prof. Mauricio Sacchi, 2014-2015)
Zhao Liu 2013- , Denoising of ground-roll wave
Kuijie Cai 2013- , Seismic superresolution
Siwei Yu 2014- , Seismic interpolation, data-driven tight frame (Visit Prof. Stanley Osher, 2014-2016)
Lina Liu 2014- , Seismic interpolation, low-rank methods (Visit Prof. Gerlind Plonka, 2015-2017)
Yongna Jia 2014- , Seismic interpolation, statistical learning (Co-advisor)
Yuhan Shui 2015- , Prony methods for seismic data analysis
Xiaojing Wang 2015- , Sparse transform
Tara Banjade 2015- , Seismic data processing
Diriba Gemechu 2015- , Seismic data processing
Hao Zhang 2016- , Seismic migration and imaging
Long Li 2016- , Variational data assimalition for oil spill dispersion
Fangshu Yang 2016- , Statistical learning
M.S. Students:
Hairong Wang, 2010-2012 (Co-advisor)
Yanfan Liu, 2011-2013 (Co-advisor), Natinal Scholarship Award in 2012
Zhao Liu, 2011-2013 (Co-advisor)
Lina Liu, 2012-2014
Yongna Jia, 2012-2014, National Scholarship Award in 2013
Shengnan Qi, 2012-2014
Qiangqiang Zhu, 2013-2015
Rui Shi, 2013-2015
Ling Liu, 2013-2015 (Co-advisor)
Xiaojing Wang, 2014-2015 (Co-advisor)
Yuhan Shui, 2014-2015
Long Li, 2014-2016 (Co-advisor), National Scholarship Award in 2015
Fangshu Yang, 2015-2016
Visitors
2015:
Chenglong Bao, National University of Singapore, Singapore
Georgy Loginov, Insitute of Petroleum Geology and Geophysics, Nobosibirsk, Russia
Hyunsun Lee, Hawaii Pacific University, USA
Jan H. Fitschen, Technische Universit?t Kaiserslautern, Germany
Johannes Persch, Technische Universitat Kaiserslautern, Germany
Ming Jiang, CEA Saclay, France
Sergey Fomel, University of Texas at Austin, USA
Jeff Shragge, University of Western Australia, Australia
Frederik Simons, Princeton University, USA
Ru-shan Wu, University of California, Santa Cruz, USA
2014:
Frorian Bossmann, University of Gottingen, Germany
Francois Le Dimet, University of Grenoble, France
2013:
Wotao Yin, University of Calfornia at Los Angeles, USA
Xiaoming Yuan, HKBU, Hongkong
Xinxin Li, HKBU, Hongkong
Wenyi Tian, HKBU, Hongkong
Chenglong Bao, National Univeristy of Singapore, Singapore
Bingsheng He, Najing University, China
Yin Shi, Northeast Petroleum University, China
2012:
Yang Liu, Jilin University, China
Jianjun Gao, China University of Geoscience, China
Shiyuan Cao, China University of Petroleum, China
Simon Beckouche, CEA Salcay, France
Soren Hauser, TU Kaiserslautern, Germany
2011:
Yi Yang, UCLA, USA
2010:
Shaharyar Khwaja, Ryerson University, Canada
2009:
Felix Herrmann, University of British Colombia, Canada
Jens Krommweh, University Duisburg-Essen, Germany
Junfeng Yang, Nanjing University, China
2008:
Wotao Yin, Rice University, USA
Francois Le Dimet, University of Grenoble 1, France
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HIT International Summer School on Pure and Applied Mathematics
News: Homepage of the Summer School http://mss.hit.edu.cn is open now!
In 2015, the international summer school on pure and applied mathematics will take place at the Harbin Institute of Technology in Harbin, China, from July 6 to August 9.
The purpose of this summer school is to introduce some of the basic ideas and state-of–the-art methods of pure and applied mathematics to graduate students and reseachers. In particular there will be ten world's leading mathematicians (3 Fields Medal, Wolf Prize, Crafoord Prize, Gauss Prize, COPSS President's Award, SIAM Kleinman Prize, SIAM Von Karman Prize, ICIAM Pioneer Prize, Former President of the International Mathematical Union, etc) to give a series of mini-courses.
Open to international students from all countries.The school will be held in English.The program features plenary talks and mini-courses by the leading international mathematicians.Hosted by the Harbin Institute of Technology, Harbin. Students are housed on the campus. Limited travel grants can be applied.The online registration (no registration fee) will start from April 15, 2015 , http://mas.hit.edu.cn.
Invited speakers:
Sir John M. Ball Oxford University
Ngo Bao Chau University of Chicago
Bjorn Engquist University of Texas at Austin
Jianqing Fan Princeton University
Vladimir Manuilov Moscow State University
Alexander Mishchenko Moscow State University
Stanley Osher University of California, Los Angeles
Stanislav Smirnov University of Geneva
Michael Unser Swiss Federal Institute of Technology in Lausanne (EPFL)
Shing-Tung Yau Harvard University
Mini-courses and Time table (to to comfirmed):
Pure Mathematics
Mini-Courses Time
Alexander MishchenkoIntroduction to differential topologyJuly 6- August
Vladimir ManuilovIntrocution to C*-algebrasJuly 6- August
Stanislav SmirnovComplex analysisJuly 13 - July 21
Shing-Tung YauDifferential geometryJuly 26 - July 30
Ngo Bao ChauNumber theory and automorphic formsJuly 30 - August 6
Applied Mathematics
Mini-coursesTime
Bjorn Engquist Mutiscale modeling and computationJuly 6 - July 12
Stanley OsherSparse recovery, optimization and applications to image science, PDE, and numerical analysisJuly 12-July 19
Jianqing FanHigh-dimensional statistical learningJuly 20-25
Michael UnserSparse stochastic processesJuly 25-July30
John BallMathematics of solid and liquid crystals
July 29-August 6
Abstract of mini-courses:
Prof. John M. Ball
Mathemetics of solid and liquid crystals
Abstract: The course will discuss static models of solid and liquid crystals. As regards solid crystals the course will concentrate on materials that undergo solid phase transformations, how these can be modelled using nonlinear elasticity, the resulting microstructure morphology, and nucleation. Concerning liquid crystals the course will describe the Oseen-Frank and Landau – de Gennes theories, and the relation between them, together with new developments concerning the description of defects. While solid and liquid crystals represent very different kinds of material, the theories used to describe them have similar variational structures, and there are interesting common issues related to the choice of function spaces, the modelling of singularities, and topology.
Prof. Ng? B?o Chau
Number theory and automorphic forms
Abstract: Though number theory is probably as old as mathematics, it is still young for it still grows vigorously.
I will lecture on problems in number theory which have motivated strong recent developments, in particular
in interaction with automorphic forms and algebraic geometry.
Prof. Jianqing Fan
High-Dimensional Statistical Learning
Abstract: High-dimensionality and Big Data characterize many contemporary statistical problems from genomics and genetics to finance and economics. We first outline a unified approach to ultrahigh dimensional variable selection problems and then focus on penalized likelihood methods which are fundamentally important building blocks to ultra-high dimensional variable selection. We will also introduce variable screening methods as well as various contemporary methods for covariance matrix estimation and graphical models. Algorithms for solving penalized likelihood methods as well as Big Data computing will also be introduced. Topics to be covered include
(1) Impact of dimensionality
(2) Penalized Least-Squares
(3) Penalized Likelihood
(4) Algorithms and Implementation
(5) Big Data computing
(g) Estimation of Large Covariance Matrices
(6) Graphical models
Prof. Bjorn Engquist
Mustiscal Modelign and Computation
Abstract: It is computationally very challenging to accurately represent the smallest scales over a domain that covers the largest scales in a multiscale problem. Classical practical cases are turbulence, high frequency wave propagation and the coupling of molecular and continuum formulations. As a background to the study of numerical multiscale methods we will consider a number of analytical techniques as, for example, homogenization theory for partial differential equations and averaging of dynamical systems. We will see how information theory can be a guide for discrete representation.
We will introduce several numerical methods for numerical simulation of multiscale problems with a focus on the framework of the Heterogeneous Multiscale Method (HMM). This is a methodology for coupling numerical simulations of different scales. A macroscale model gets microscale data from detailed computations on smaller subsets of the full domain. We will study HMM both in the partial differential and dynamical systems settings. Various applications, for example, to epitaxial growth, crack propagation and flow in porous media will be presented. High frequency wave propagation will be approximated both by HMM and by more traditional fest methods.
Prof. Vladimir Manuilov
Introduction to C*-Algebras
Abstract: C*-algebras were introduced by Gelfand and Naimark in 1943, and are now an important part of functional analysis with many applications in harmonic analysis and representation theory, non-commutative geometry and topology (it is often said that the C*-algebra theory is non-commutative topology), and mathematical physics (quantum mechanics and field theory, statistical physics).
The aim of this course is to lay the foundations for further studies of the subject and its applications.
The following subjects will be covered:
(1) Spectral theory
(2) Commutative C*-algebras
(3) deals, quotients, homomorphisms
(4) States, representations, Gelfand-Naimark-Segal Theorem
(5) Some interesting classes and examples of C*-algebras
(6) Constructions for C*-algebras (pull-backs, tensor and free products etc.)
If time permits, an introduction to K-theory for C*-algebras will be given.
Prerequisites: standard bachelor courses on analysis and topology, plus some knowledge in functional analysis (Hilbert space operators).
Prof. Aleksander Mishchen
Introduction of Differential topology
Abstract: Differential topology may be defined as the study of those properties of differentiable manifolds which are invariant under diffeomorphism (differentiable homeomorphism). Typical problem falling under this heading are the following:
(1) Given two differentiable manifolds, under what conditions are they diffeomorphic?
(2) Given a differentiable manifold, is it the boundary of some differentiable manifold with boundary?
(3) Given a differentiable manifold, is it parallelisable?
All these problems concern more than the topology of the manifold, yet they do not belong to differential geometry, which usually assumes additional structure (e.g., a connection or a metric). The most powerful tools in this subject have been derived from the methods of algebraic topology. In particular, the theory of characteristic classes is crucial, where-by one passes from the manifold M to its tangent bundle, and thence to a cohomology class in M which depends on this bundle.
Outline:
1. Smooth manifolds.
2. Tangent bundles.
3. Bundles. Vector bundles.
4. Calculus on smooth manifolds. Differential Foems.
5. Homology and Cohomology. De Rham Cohomology.
6. Connections and Curvatures.
5. Characteristic classes. Chern-Weil Theory.
6. Immersions and embeddings. Bordisms
7. Surgery. Smooth structures on homotopy type.
Prerequisites: standard bachelor courses on analysis, algebra and topology.
Prof. Stanley Osher
Sparse Recovery, Optimization, and Applications to Image and Information Science, Applied Partial Differential Equations
and Numerical Analysis
Abstract: Our modern world is dominated by visual communication via digital images and videos. This raises a variety of novel questions in mathematics. Tasks such as improving visual quality, detecting objects, or detecting motion become increasingly relevant and need to be automated due to the ongoing flood of data. Among the techniques used for these problems, variational methods play a prominent role. Simultaneously, exploiting sparsity has become a very important task in data science. A sparse signal is one which has very few nonzero elements or becomes so under a change of basis or through a transform. Compressed sensing and regularized inverse problems have made variational methods of the type used in imaging even more important. Moreover, continuous analogues of these discrete optimization problems are becoming more useful in applied partial differential equations and their numerical solution.
We will review the basics and cover recent research in this area.
Prof. Michael Unser
Sparse stochastic processes
Abstract: Sparse stochastic processes are continuous-domain processes that admit a parsimonious representation in some matched wavelet-like basis. Such models are relevant for image compression, compressed sensing, and, more generally, for the derivation of statistical algorithms for solving ill-posed inverse problems.
This course is devoted to the study of the broad family of sparse processes that are specified by a generic (non-Gaussian) innovation model or, equivalently, as solutions of linear stochastic differential equations driven by white Lévy noise. It presents the mathematical tools for their characterization. The two leading threads that underly the exposition are
- the statistical property of infinite divisibility, which induces two distinct types of behavior Gaussian vs. sparse at the exclusion of any other;
- the structural link between linear stochastic processes and spline functions which is exploited to simplify the mathematics.
The concepts are illustrated with the derivation of algorithms for the recovery of sparse signals, with applications to biomedical image reconstruction. In particular, this leads to a Bayesian reinterpretation of popular sparsity-promoting processing schemes such as total-variation denoising, LASSO, and wavelet shrinkage—as MAP estimators for specific types of sparse processes. The formulation also suggests alternative recovery procedures that minimize the estimation error.
The course is targeted to an audience of graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.
For more details, including table of content, see http://www.sparseprocesses.org.
We look forward to welcoming you to Harbin this July!
Contacting Organizers:
Jianwei Ma (Department of Mathematics, jma@hit.edu.cn)
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Data-driven tight frame (数据驱动紧框架)
Data-driven tight frame (DDTF)是一种新的自适应字典学习方法，它对字典元素给予了一个“紧框架”的约束（“紧框架”是一个比“正交”更松弛的约束条件，小波和曲波变换都属于“紧框架”字典），其学习过程的计算效率比传统的字典学习方法（如K-SVD）要快10-100倍。DDTF可作为地震数据稀疏表示的基本工具。目前我们已把其用在高维（3D和5D）地震勘探数据的去噪和插值。并相继提出了基于Monte Carlo (蒙特卡洛）DDTF、张量积DDTF、与Seislet结合的双稀疏学习字典，来进一步提高计算效率和利用高维数据的几何结构性。
(a): 输入的地震数据; (b): 输入的初始滤波器; (c): DDTF学习得到的滤波器
1. L. Liu, J. Ma*, J.-F. Cai, Data-driven Kronecker tight frame and applications in seismic data interpolation and denoising, 2016, Geophysics, submitted.
2. Y. Chen, J. Ma*, S. Fomel, Double sparsity dictionary for seismic noise attenuation, Geophysics, 2016, 81 (2), V17-V30.
3. S. Yu, J. Ma*, S. Osher, Monte Carlo data-driven tight frame for seismic data recovery, Geophysics, 2016, accepted.
4. S. Yu, J. Ma*, X. Zhang, M. Sacchi, Denoising and interpolation of high-dimensional seismic data by learning a tight frame, Geophysics, 2015, 80 (5), V119-V132.
5. J. Liang, J. Ma*, X. Zhang, Seismic data restoration via data-driven tight frame, Geophysics, 2014, 79 (3), V65-V74.
Asymmetric Chirplet Transform (不对称C-子波变换)
由于地层的吸收，地震信号在时间序列上表现为不对称衰减。 为了能更好的稀疏表示地震信号，人们在构造稀疏变换的时候，一个基本的动机应该是去设计不对称衰减的基元。像傅里叶变换的周期基元（正玄函数）就不能很好表示这种衰减的非稳定态信号。我们提出了一种不对称C-子波变换，其基元具有自适应的非对称快速衰减的特性，可用于地震信号的分解和处理。变换的参数/系数如：包络线、旅行时、局部相位和频率均带有很强的物理意义。对地震解释、时变子波提取、波形反演均有可用武之地。
C-子波变换的部分基元展示 对1D信号（红色线）做C-子波变换得到的相位参数（蓝色线）
1. F. Bossmann, J. Ma*, Asymmetric chirplet transform II: phase, frequency, and chirp rate, 2015, Geophysics, submitted.
2. F. Bossmann, J. Ma*, Asymmetric chirplet transform for sparse representation of seismic data, Geophysics, 2015, 80 (6), WD89-WD100.
Geometric Mode Decomposition
针对著名的经验模态分解方法(EMD: Empirical Mode Decomposition）对含陡倾角等特征的地震数据处理的不足，提出了变分模态分解(VMD: Variational Mode Decomposition)，并在扩展到多维的过程中，进一步创新提出了几何模态分解（GMD: Geometrical Mode Decomposition）。此方法对波场特征分离、面波或多次被压制、插值等工作上都可能发挥作用。
基于新方法的地震数据分解、去噪和插值示例
1. S. Yu, J. Ma*, S. Osher, Variational model decomposition of seismic data, 2016, Geophysics, submitted.
基于分裂算法的快速高阶有限差分格式
传统高阶紧支有限差分方法具有高精度的优点，但由于要求解多对角矩阵的逆，导致计算速度慢。我们采用了三种分裂算法，通过把多对角阵分解成多个三对角阵来提高波动方程模拟的计算速度，而且还保留了其计算精度高的优点；并与8、18、60阶的显式差分格式做了算例比较。
Sigsbee model
SEG salt model
SEG Salt model
1. C. Zhang, B. Sun, J. Ma, H. Yang, Splitting algorithm for high-order compact finite difference scheme in wave equation modeling, Geophysics, 2015, revised
压缩感知走进地球物理勘探（随笔）
什么是压缩感知？
压缩感知（CS：Compressed Sensing）描述的是：可从高度不完备的线性测量中高精度重构未知目标。创造性的把L1范数最小化和随机矩阵理论有机结合，可得到稀疏信号重建的最佳效果。它讲述了要重构一个连续信号，不再与香侬-奈奎斯特采样定理所说的“频带”有关，而是与未知信号的“稀疏度”有关。CS于2004年由E. Candes, D. Donoho, T. Tao 三位著名数学家提出，其中华裔天才数学家T.Tao (陶哲轩)的智商据说超过爱因斯坦，是人类史上智商最高的神人。CS技术入选了2007年美国十大科技进展。CS的主要几篇文献已被引用四万多次。
从数学上讲，CS本质是降维，从低维空间去研究高维空间。
从信号上讲，CS本质是采样，从频率相关到稀疏度相关。
从工程上讲，CS本质是成本，从物理测量成本转移到数学计算成本。
为什么CS这么火？
从二十世纪四十年代开始，统治信号和信息领域的是香侬-奈奎斯特采样定理：要想从离散信号去恢复连续信号，离散信号的采样率至少应该是此连续信号最高频率的两倍。可以说大部分的信号采集相关的设备都是基于香侬定理设计的，如相机、雷达、核磁共振等等。CS理论出现后，其采样率将不在和信号的频带有关，而是和信号的稀疏度有关，打破了传统理论的束缚。所以很多基于传统理论设计的方法、软件和设备都可以得到升级换代。 单像素相机就是一个很好的例子，其反其道而行之，不再去追求千万像素的分辨率，而是用一个像素的时间序列成像就可用重构出高分辨率图像。
为什么CS能走进勘探？
压缩感知是一个理论框架，不是单一的技术，其三要素是随机测量，目标的稀疏表示，稀疏促进的优化重构算法。地震勘探中，降低野外数据采集的成本而又能保证勘探精度，是一个很重要的问题。由于CS理论的出现，这个问题的考虑也由香侬采样的思路转变到稀疏重构的思路上来，会带来很多新的变革。CS所涉及的技术，在勘探的采集、处理、正演模拟、成像反演等方面都会带来改进或冲击。但其核心问题之一还是地震数据和地质目标的稀疏表示！
淘宝的成功，在于改变了人们的交易方式。
腾讯的成功，在于改变了人们的沟通方式。
谷歌的成功，在于改变了人们的搜索方式。
C S 的成功，在于改变了信号的采集方式。
我所接触到的CS：
1998年在清华大学读博期间开始学习小波变换，从事波动方程多尺度模拟的研究。 2002年博士毕业后，在欧洲转做脊波和曲波变换的信号处理。这两个几何小波变换正是压缩感知的创始人E. Candes的博士论文工作（导师是D. Donoho）。2006年在瑞士洛桑联邦理工的小波会议上，首次接触到压缩感知的报告，并有幸与Candes教授当面交流了自己在曲波变换方面的工作。2006年回国后，即刻着手学习压缩感知的知识。2007年，面向我国“嫦娥”探月卫星发射的大背景，成功将CS应用到了遥感成像。随即认识了Wotao Yin和Felix Herrmann 等较早从事CS的专家，并于2008年派出课题组学生唐刚前往加拿大UBC访问 Felix Herrmann教授一年，逐步开始CS在地震数据插值的应用。在2009年的地球物理年会上，做了题为“曲波变换和压缩感知在地震勘探中的成就和前景”的报告，2010年在著名期刊《IEEE Signal Processing Magazine》撰写了曲波变换的邀请综述， 在2011地球物理年会的“傅承义”获奖大会报告中做了“稀疏促进地震勘探”，并受《信号处理》期刊的邀请撰写了一篇中文的综述论文：压缩感知及其应用－从稀疏约束到低秩约束优化。
2011年到哈工大以后，课题组将工作重心放在CS在勘探中的创新应用。本着“不跟随”“自主创新“的理念，重点攻关CS的核心技术：地震数据的稀疏表示。课题组在该方向也相继取得了数据驱动紧框架、不对称C-子波变换、几何模式分解、双稀疏变换等技术，并在去噪、插值等方面得到较好应用。 目前正逐步开展地质目标导向的统计学习、基于CS的波动方程快速模拟和逆时偏移成像。
2013年在哈尔滨举办了优化和压缩感知的国际会议，2015年1月和S. Fomel, M. Sacchi, Ru-shan Wu共同在哈尔滨举办了以稀疏促进勘探为主题的第一届数学地球物理会议。2015年8月，与S. Fomel共同在每四年一届的ICIAM（国际工业与应用数学大会）上开设了压缩感知勘探为主题的专题讨论会。
2015年12月2-4号，在李幼铭研究员和张捷教授等前辈推动下，SEG（国际勘探地球物理学家协会）在北京成功举办了SEG压缩感知－地球物理应用新技术研讨会，注册参会人数达到180人。本人也有幸担任会议的两位主席之一，共同见证了近30位分别来自产、学、研的国内外专家的精彩报告。12月29号小范围CS研讨会在浙大继续召开，并在全国同时开设了18个视频分会场。目前CS已得到中石油和中石化等行业巨头的关注。
期待2016年，CS能真正走进我国油气勘探，去起到它应有的作用，也为当前油气行业寒冬带去一丝暖意。
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压缩感知走进地球物理勘探（随笔）
什么是压缩感知？
压缩感知（CS：Compressed Sensing）描述的是：可从高度不完备的线性测量中高精度重构未知目标。创造性的把L1范数最小化和随机矩阵理论有机结合，可得到稀疏信号重建的最佳效果。它讲述了要重构一个连续信号，不再与香侬-奈奎斯特采样定理所说的“频带”有关，而是与未知信号的“稀疏度”有关。CS于2004年由E. Candes, D. Donoho, T. Tao 三位著名数学家提出，其中华裔天才数学家T.Tao (陶哲轩)的智商据说超过爱因斯坦，是人类史上智商最高的神人。CS技术入选了2007年美国十大科技进展。CS的主要几篇文献已被引用四万多次。
从数学上讲，CS本质是降维，从低维空间去研究高维空间。
从信号上讲，CS本质是采样，从频率相关到稀疏度相关。
从工程上讲，CS本质是成本，从物理测量成本转移到数学计算成本。
为什么CS这么火？
从二十世纪四十年代开始，统治信号和信息领域的是香侬-奈奎斯特采样定理：要想从离散信号去恢复连续信号，离散信号的采样率至少应该是此连续信号最高频率的两倍。可以说大部分的信号采集相关的设备都是基于香侬定理设计的，如相机、雷达、核磁共振等等。CS理论出现后，其采样率将不在和信号的频带有关，而是和信号的稀疏度有关，打破了传统理论的束缚。所以很多基于传统理论设计的方法、软件和设备都可以得到升级换代。 单像素相机就是一个很好的例子，其反其道而行之，不再去追求千万像素的分辨率，而是用一个像素的时间序列成像就可用重构出高分辨率图像。
为什么CS能走进勘探？
压缩感知是一个理论框架，不是单一的技术，其三要素是随机测量，目标的稀疏表示，稀疏促进的优化重构算法。地震勘探中，降低野外数据采集的成本而又能保证勘探精度，是一个很重要的问题。由于CS理论的出现，这个问题的考虑也由香侬采样的思路转变到稀疏重构的思路上来，会带来很多新的变革。CS所涉及的技术，在勘探的采集、处理、正演模拟、成像反演等方面都会带来改进或冲击。但其核心问题之一还是地震数据和地质目标的稀疏表示！
淘宝的成功，在于改变了人们的交易方式。
腾讯的成功，在于改变了人们的沟通方式。
谷歌的成功，在于改变了人们的搜索方式。
C S 的成功，在于改变了信号的采集方式。
我所接触到的CS：
1998年在清华大学读博期间开始学习小波变换，从事波动方程多尺度模拟的研究。 2002年博士毕业后，在欧洲转做脊波和曲波变换的信号处理。这两个几何小波变换正是压缩感知的创始人E. Candes的博士论文工作（导师是D. Donoho）。2006年在瑞士洛桑联邦理工的小波会议上，首次接触到压缩感知的报告，并有幸与Candes教授当面交流了自己在曲波变换方面的工作。2006年回国后，即刻着手学习压缩感知的知识。2007年，面向我国“嫦娥”探月卫星发射的大背景，成功将CS应用到了遥感成像。随即认识了Wotao Yin和Felix Herrmann 等较早从事CS的专家，并于2008年派出课题组学生唐刚前往加拿大UBC访问 Felix Herrmann教授一年，逐步开始CS在地震数据插值的应用。在2009年的地球物理年会上，做了题为“曲波变换和压缩感知在地震勘探中的成就和前景”的报告，2010年在著名期刊《IEEE Signal Processing Magazine》撰写了曲波变换的邀请综述， 在2011地球物理年会的“傅承义”获奖大会报告中做了“稀疏促进地震勘探”，并受《信号处理》期刊的邀请撰写了一篇中文的综述论文：压缩感知及其应用－从稀疏约束到低秩约束优化。
2011年到哈工大以后，课题组将工作重心放在CS在勘探中的创新应用。本着“不跟随”“自主创新“的理念，重点攻关CS的核心技术：地震数据的稀疏表示。课题组在该方向也相继取得了数据驱动紧框架、不对称C-子波变换、几何模式分解、双稀疏变换等技术，并在去噪、插值等方面得到较好应用。 目前正逐步开展地质目标导向的统计学习、基于CS的波动方程快速模拟和逆时偏移成像。
2013年在哈尔滨举办了优化和压缩感知的国际会议，2015年1月和S. Fomel, M. Sacchi, Ru-shan Wu共同在哈尔滨举办了以稀疏促进勘探为主题的第一届数学地球物理会议。2015年8月，与S. Fomel共同在每四年一届的ICIAM（国际工业与应用数学大会）上开设了压缩感知勘探为主题的专题讨论会。
2015年12月2-4号，在李幼铭研究员和张捷教授等前辈推动下，SEG（国际勘探地球物理学家协会）在北京成功举办了SEG压缩感知－地球物理应用新技术研讨会，注册参会人数达到180人。本人也有幸担任会议的两位主席之一，共同见证了近30位分别来自产、学、研的国内外专家的精彩报告。12月29号小范围CS研讨会在浙大继续召开，并在全国同时开设了18个视频分会场。目前CS已得到中石油和中石化等行业巨头的关注。
期待2016年，CS能真正走进我国油气勘探，去起到它应有的作用，也为当前油气行业寒冬带去一丝暖意。
