POSITION/TITLE
Professor
EDUCATION BACKGROUND
Ph.D. in Operations Research and Control Theory, Chinese Academy of Sciences, 1998
RESEARCH FIELD
Applied Mathematics, Mathematical Optimization, Sparse Signal Reconstruction, Compressed Sensing, Linear Inverse Problems and Numerical Analysis
BIOGRAPHY
Yun-Bin Zhao is currently a Professor (Adjunct) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He is also a Professor of the Shenzhen Research Institute of Big Data (SRIBD). He received his Ph.D. degree in operations research and control theory in 1998 from the Chinese Academy of Sciences. From 2003 to 2007, he was an Associate Professor in the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He joined to the University of Birmingham, UK in 2007 as a Lecturer and then Associate Professor since 2012. His working areas include applied mathematics, operations research, computational optimization, numerical linear algebra, compressed sensing, signal and image processing. His latest research is to develop efficient computational methods for sparse optimization problems and their applications to signal processing. He is the author of the book “Sparse Optimization Theory and Methods”, CRC Press/Taylor & Francis Group, 2018.
ACADEMIC PUBLICATIONS
A. Books
1. Y.B. Zhao, “Sparse Optimization Theory and Methods, CRC Press, Boca Raton, FL, 2018.
B. Journal articles
1. Y.B. Zhao and Z.-Q. Luo, Natural thresholding algorithms for signal recovery with sparsity, IEEE Open Journal of Signal Processing, 3 (2022), 417-431.
2. N. Meng, Y.B. Zhao, M. Kocvara and F. Sun, Partial gradient optimal thresholding algorithms for a class of sparse optimization problems, Journal of Global Optimization, 84 (2022), 393–413.
3. Y.B. Zhao and Z.-Q. Luo, Analysis of optimal thresholding algorithms for compressed sensing, Signal Processing, 187 (2021), 108148.
4. N. Meng and Y.B. Zhao, Newton-step-based hard thresholding algorithms for sparse signal recovery, IEEE Transactions on Signal Processing, 68 (2020), 6594-6606.
5. Y.B. Zhao, Optimal k-thresholding algorithms for sparse optimization problems, SIAM Journal on Optimization, 30 (2020), no. 1, 31-55
6. Y.B. Zhao, H. Jiang and Z.-Q. Luo, Weak stability of $\ell$-minimization methods in sparse data reconstruction, Mathematics of Operations Research, 44 (2019), no.1, 173–195.
7. Y.B. Zhao, Z.Q. Luo, Constructing new weighted L1-algorithms for the sparsest points of polyhedral sets, Mathematics of Operations Research, 42 (2017), no.1, 57--76.
8. Y.B. Zhao and M. Kocvara, A new computational method for the sparsest solutions to systems of linear equations, SIAM Journal on Optimization, 25 (2015), no.2, 1110-1134
9. Y.B. Zhao, Equivalence and strong equivalence of sparsest and least l1-norm nonnegative solutions to linear systems and their application, Journal of the Operations Research Society of China, 2 (2014), 171-193.
10. Y.B. Zhao, RSP-based analysis for sparsest and least l1-norms solutions to underdetermined linear systems, IEEE Transactions on Signal Processing, 61 (2013), no.22, 5777-5788
11. Y.B. Zhao and D. Li, Reweighted l1-minimization for sparse solutions to underdetermined linear systems, SIAM Journal on Optimization, 22 (2012), no. 3, 1065-1088.
12 Y.B. Zhao, An approximation theory of matrix rank minimization and its application to quadratic equations, Linear Algebra and Its Applications, 437 (2012), no. 1, 77--93.
13. Y.B. Zhao, Convexity condition and Legendre-Fenchel transform of the product of finitely many quadratic forms, Applied Mathematics and Optimization, 62 (2010), no.3, 411--434.
14. Y.B. Zhao, The Legendre-Fenchel conjugate of the product of two positive-definite quadratic forms, SIAM Journal on Matrix Analysis and Applications, 31 (2010). no. 4, 1792-1811.
15. I. Averbakh and Y.B. Zhao, Explicit reformulation for robust mathematical optimization with general uncertainty sets, SIAM Journal on Optimization, 18 (2008), no. 4, 1436--1466.
16. Y.B. Zhao, S.C. Fang and D. Li, Constructing generalized mean functions using convex functions with regularity, SIAM Journal On Optimization, 17 (2006), no. 1, 37-51.
17. J. Peng, T. Terlaky and Y.B. Zhao, A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function. SIAM Journal on Optimization, 15 (2005), no. 4, 1105--1127.
18. Y.B. Zhao and D. Li, A globally and locally superlinearly convergent non-interior-point algorithm for P0 LCPs, SIAM Journal on Optimization, 13 (2003), no. 4, 1195-1221.
19. Y.B. Zhao and D. Li, Locating the least 2-norm solution of linear programs via a path-following method, SIAM Journal on Optimization, 12 (2002), no. 4, 893-912.
20. Y.B. Zhao and D. Li, Existence and limiting behavior of a non-interior-point trajectory for nonlinear complementarity problems without strict feasibility condition. SIAM Journal on Control and Optimization, 40 (2001), no. 3, 898-924.
21. Y.B. Zhao and D. Li, On a new homotopy continuation trajectory for nonlinear complementary problems. Mathematics of Operations Research, 26 (2001), no. 1, 119--146
22. Y.B. Zhao and D. Li, Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM Journal on Optimization, 11 (2001), no. 4, 962--973.
23. Y.B. Zhao and G. Isac, Properties of a multi-valued mapping associated with some non-monotone complementarity problems. SIAM Journal on Control and Optimization, 39 (2000), no. 2, 571-593.
POSITION/TITLE
Professor
EDUCATION BACKGROUND
Ph.D. in Operations Research and Control Theory, Chinese Academy of Sciences, 1998
RESEARCH FIELD
Applied Mathematics, Mathematical Optimization, Sparse Signal Reconstruction, Compressed Sensing, Linear Inverse Problems and Numerical Analysis
BIOGRAPHY
Yun-Bin Zhao is currently a Professor (Adjunct) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He is also a Professor of the Shenzhen Research Institute of Big Data (SRIBD). He received his Ph.D. degree in operations research and control theory in 1998 from the Chinese Academy of Sciences. From 2003 to 2007, he was an Associate Professor in the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He joined to the University of Birmingham, UK in 2007 as a Lecturer and then Associate Professor since 2012. His working areas include applied mathematics, operations research, computational optimization, numerical linear algebra, compressed sensing, signal and image processing. His latest research is to develop efficient computational methods for sparse optimization problems and their applications to signal processing. He is the author of the book “Sparse Optimization Theory and Methods”, CRC Press/Taylor & Francis Group, 2018.
ACADEMIC PUBLICATIONS
A. Books
1. Y.B. Zhao, “Sparse Optimization Theory and Methods, CRC Press, Boca Raton, FL, 2018.
B. Journal articles
1. Y.B. Zhao and Z.-Q. Luo, Natural thresholding algorithms for signal recovery with sparsity, IEEE Open Journal of Signal Processing, 3 (2022), 417-431.
2. N. Meng, Y.B. Zhao, M. Kocvara and F. Sun, Partial gradient optimal thresholding algorithms for a class of sparse optimization problems, Journal of Global Optimization, 84 (2022), 393–413.
3. Y.B. Zhao and Z.-Q. Luo, Analysis of optimal thresholding algorithms for compressed sensing, Signal Processing, 187 (2021), 108148.
4. N. Meng and Y.B. Zhao, Newton-step-based hard thresholding algorithms for sparse signal recovery, IEEE Transactions on Signal Processing, 68 (2020), 6594-6606.
5. Y.B. Zhao, Optimal k-thresholding algorithms for sparse optimization problems, SIAM Journal on Optimization, 30 (2020), no. 1, 31-55
6. Y.B. Zhao, H. Jiang and Z.-Q. Luo, Weak stability of $\ell$-minimization methods in sparse data reconstruction, Mathematics of Operations Research, 44 (2019), no.1, 173–195.
7. Y.B. Zhao, Z.Q. Luo, Constructing new weighted L1-algorithms for the sparsest points of polyhedral sets, Mathematics of Operations Research, 42 (2017), no.1, 57--76.
8. Y.B. Zhao and M. Kocvara, A new computational method for the sparsest solutions to systems of linear equations, SIAM Journal on Optimization, 25 (2015), no.2, 1110-1134
9. Y.B. Zhao, Equivalence and strong equivalence of sparsest and least l1-norm nonnegative solutions to linear systems and their application, Journal of the Operations Research Society of China, 2 (2014), 171-193.
10. Y.B. Zhao, RSP-based analysis for sparsest and least l1-norms solutions to underdetermined linear systems, IEEE Transactions on Signal Processing, 61 (2013), no.22, 5777-5788
11. Y.B. Zhao and D. Li, Reweighted l1-minimization for sparse solutions to underdetermined linear systems, SIAM Journal on Optimization, 22 (2012), no. 3, 1065-1088.
12 Y.B. Zhao, An approximation theory of matrix rank minimization and its application to quadratic equations, Linear Algebra and Its Applications, 437 (2012), no. 1, 77--93.
13. Y.B. Zhao, Convexity condition and Legendre-Fenchel transform of the product of finitely many quadratic forms, Applied Mathematics and Optimization, 62 (2010), no.3, 411--434.
14. Y.B. Zhao, The Legendre-Fenchel conjugate of the product of two positive-definite quadratic forms, SIAM Journal on Matrix Analysis and Applications, 31 (2010). no. 4, 1792-1811.
15. I. Averbakh and Y.B. Zhao, Explicit reformulation for robust mathematical optimization with general uncertainty sets, SIAM Journal on Optimization, 18 (2008), no. 4, 1436--1466.
16. Y.B. Zhao, S.C. Fang and D. Li, Constructing generalized mean functions using convex functions with regularity, SIAM Journal On Optimization, 17 (2006), no. 1, 37-51.
17. J. Peng, T. Terlaky and Y.B. Zhao, A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function. SIAM Journal on Optimization, 15 (2005), no. 4, 1105--1127.
18. Y.B. Zhao and D. Li, A globally and locally superlinearly convergent non-interior-point algorithm for P0 LCPs, SIAM Journal on Optimization, 13 (2003), no. 4, 1195-1221.
19. Y.B. Zhao and D. Li, Locating the least 2-norm solution of linear programs via a path-following method, SIAM Journal on Optimization, 12 (2002), no. 4, 893-912.
20. Y.B. Zhao and D. Li, Existence and limiting behavior of a non-interior-point trajectory for nonlinear complementarity problems without strict feasibility condition. SIAM Journal on Control and Optimization, 40 (2001), no. 3, 898-924.
21. Y.B. Zhao and D. Li, On a new homotopy continuation trajectory for nonlinear complementary problems. Mathematics of Operations Research, 26 (2001), no. 1, 119--146
22. Y.B. Zhao and D. Li, Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM Journal on Optimization, 11 (2001), no. 4, 962--973.
23. Y.B. Zhao and G. Isac, Properties of a multi-valued mapping associated with some non-monotone complementarity problems. SIAM Journal on Control and Optimization, 39 (2000), no. 2, 571-593.
POSITION/TITLE
Professor
EDUCATION BACKGROUND
Ph.D. in Operations Research and Control Theory, Chinese Academy of Sciences, 1998
RESEARCH FIELD
Applied Mathematics, Mathematical Optimization, Sparse Signal Reconstruction, Compressed Sensing, Linear Inverse Problems and Numerical Analysis
BIOGRAPHY
Yun-Bin Zhao is currently a Professor (Adjunct) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He is also a Professor of the Shenzhen Research Institute of Big Data (SRIBD). He received his Ph.D. degree in operations research and control theory in 1998 from the Chinese Academy of Sciences. From 2003 to 2007, he was an Associate Professor in the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He joined to the University of Birmingham, UK in 2007 as a Lecturer and then Associate Professor since 2012. His working areas include applied mathematics, operations research, computational optimization, numerical linear algebra, compressed sensing, signal and image processing. His latest research is to develop efficient computational methods for sparse optimization problems and their applications to signal processing. He is the author of the book “Sparse Optimization Theory and Methods”, CRC Press/Taylor & Francis Group, 2018.
ACADEMIC PUBLICATIONS
A. Books
1. Y.B. Zhao, “Sparse Optimization Theory and Methods, CRC Press, Boca Raton, FL, 2018.
B. Journal articles
1. Y.B. Zhao and Z.-Q. Luo, Natural thresholding algorithms for signal recovery with sparsity, IEEE Open Journal of Signal Processing, 3 (2022), 417-431.
2. N. Meng, Y.B. Zhao, M. Kocvara and F. Sun, Partial gradient optimal thresholding algorithms for a class of sparse optimization problems, Journal of Global Optimization, 84 (2022), 393–413.
3. Y.B. Zhao and Z.-Q. Luo, Analysis of optimal thresholding algorithms for compressed sensing, Signal Processing, 187 (2021), 108148.
4. N. Meng and Y.B. Zhao, Newton-step-based hard thresholding algorithms for sparse signal recovery, IEEE Transactions on Signal Processing, 68 (2020), 6594-6606.
5. Y.B. Zhao, Optimal k-thresholding algorithms for sparse optimization problems, SIAM Journal on Optimization, 30 (2020), no. 1, 31-55
6. Y.B. Zhao, H. Jiang and Z.-Q. Luo, Weak stability of $\ell$-minimization methods in sparse data reconstruction, Mathematics of Operations Research, 44 (2019), no.1, 173–195.
7. Y.B. Zhao, Z.Q. Luo, Constructing new weighted L1-algorithms for the sparsest points of polyhedral sets, Mathematics of Operations Research, 42 (2017), no.1, 57--76.
8. Y.B. Zhao and M. Kocvara, A new computational method for the sparsest solutions to systems of linear equations, SIAM Journal on Optimization, 25 (2015), no.2, 1110-1134
9. Y.B. Zhao, Equivalence and strong equivalence of sparsest and least l1-norm nonnegative solutions to linear systems and their application, Journal of the Operations Research Society of China, 2 (2014), 171-193.
10. Y.B. Zhao, RSP-based analysis for sparsest and least l1-norms solutions to underdetermined linear systems, IEEE Transactions on Signal Processing, 61 (2013), no.22, 5777-5788
11. Y.B. Zhao and D. Li, Reweighted l1-minimization for sparse solutions to underdetermined linear systems, SIAM Journal on Optimization, 22 (2012), no. 3, 1065-1088.
12 Y.B. Zhao, An approximation theory of matrix rank minimization and its application to quadratic equations, Linear Algebra and Its Applications, 437 (2012), no. 1, 77--93.
13. Y.B. Zhao, Convexity condition and Legendre-Fenchel transform of the product of finitely many quadratic forms, Applied Mathematics and Optimization, 62 (2010), no.3, 411--434.
14. Y.B. Zhao, The Legendre-Fenchel conjugate of the product of two positive-definite quadratic forms, SIAM Journal on Matrix Analysis and Applications, 31 (2010). no. 4, 1792-1811.
15. I. Averbakh and Y.B. Zhao, Explicit reformulation for robust mathematical optimization with general uncertainty sets, SIAM Journal on Optimization, 18 (2008), no. 4, 1436--1466.
16. Y.B. Zhao, S.C. Fang and D. Li, Constructing generalized mean functions using convex functions with regularity, SIAM Journal On Optimization, 17 (2006), no. 1, 37-51.
17. J. Peng, T. Terlaky and Y.B. Zhao, A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function. SIAM Journal on Optimization, 15 (2005), no. 4, 1105--1127.
18. Y.B. Zhao and D. Li, A globally and locally superlinearly convergent non-interior-point algorithm for P0 LCPs, SIAM Journal on Optimization, 13 (2003), no. 4, 1195-1221.
19. Y.B. Zhao and D. Li, Locating the least 2-norm solution of linear programs via a path-following method, SIAM Journal on Optimization, 12 (2002), no. 4, 893-912.
20. Y.B. Zhao and D. Li, Existence and limiting behavior of a non-interior-point trajectory for nonlinear complementarity problems without strict feasibility condition. SIAM Journal on Control and Optimization, 40 (2001), no. 3, 898-924.
21. Y.B. Zhao and D. Li, On a new homotopy continuation trajectory for nonlinear complementary problems. Mathematics of Operations Research, 26 (2001), no. 1, 119--146
22. Y.B. Zhao and D. Li, Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM Journal on Optimization, 11 (2001), no. 4, 962--973.
23. Y.B. Zhao and G. Isac, Properties of a multi-valued mapping associated with some non-monotone complementarity problems. SIAM Journal on Control and Optimization, 39 (2000), no. 2, 571-593.
POSITION/TITLE
Professor
EDUCATION BACKGROUND
Ph.D. in Operations Research and Control Theory, Chinese Academy of Sciences, 1998
RESEARCH FIELD
Applied Mathematics, Mathematical Optimization, Sparse Signal Reconstruction, Compressed Sensing, Linear Inverse Problems and Numerical Analysis
BIOGRAPHY
Yun-Bin Zhao is currently a Professor (Adjunct) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He is also a Professor of the Shenzhen Research Institute of Big Data (SRIBD). He received his Ph.D. degree in operations research and control theory in 1998 from the Chinese Academy of Sciences. From 2003 to 2007, he was an Associate Professor in the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He joined to the University of Birmingham, UK in 2007 as a Lecturer and then Associate Professor since 2012. His working areas include applied mathematics, operations research, computational optimization, numerical linear algebra, compressed sensing, signal and image processing. His latest research is to develop efficient computational methods for sparse optimization problems and their applications to signal processing. He is the author of the book “Sparse Optimization Theory and Methods”, CRC Press/Taylor & Francis Group, 2018.
ACADEMIC PUBLICATIONS
A. Books
1. Y.B. Zhao, “Sparse Optimization Theory and Methods, CRC Press, Boca Raton, FL, 2018.
B. Journal articles
1. Y.B. Zhao and Z.-Q. Luo, Natural thresholding algorithms for signal recovery with sparsity, IEEE Open Journal of Signal Processing, 3 (2022), 417-431.
2. N. Meng, Y.B. Zhao, M. Kocvara and F. Sun, Partial gradient optimal thresholding algorithms for a class of sparse optimization problems, Journal of Global Optimization, 84 (2022), 393–413.
3. Y.B. Zhao and Z.-Q. Luo, Analysis of optimal thresholding algorithms for compressed sensing, Signal Processing, 187 (2021), 108148.
4. N. Meng and Y.B. Zhao, Newton-step-based hard thresholding algorithms for sparse signal recovery, IEEE Transactions on Signal Processing, 68 (2020), 6594-6606.
5. Y.B. Zhao, Optimal k-thresholding algorithms for sparse optimization problems, SIAM Journal on Optimization, 30 (2020), no. 1, 31-55
6. Y.B. Zhao, H. Jiang and Z.-Q. Luo, Weak stability of $\ell$-minimization methods in sparse data reconstruction, Mathematics of Operations Research, 44 (2019), no.1, 173–195.
7. Y.B. Zhao, Z.Q. Luo, Constructing new weighted L1-algorithms for the sparsest points of polyhedral sets, Mathematics of Operations Research, 42 (2017), no.1, 57--76.
8. Y.B. Zhao and M. Kocvara, A new computational method for the sparsest solutions to systems of linear equations, SIAM Journal on Optimization, 25 (2015), no.2, 1110-1134
9. Y.B. Zhao, Equivalence and strong equivalence of sparsest and least l1-norm nonnegative solutions to linear systems and their application, Journal of the Operations Research Society of China, 2 (2014), 171-193.
10. Y.B. Zhao, RSP-based analysis for sparsest and least l1-norms solutions to underdetermined linear systems, IEEE Transactions on Signal Processing, 61 (2013), no.22, 5777-5788
11. Y.B. Zhao and D. Li, Reweighted l1-minimization for sparse solutions to underdetermined linear systems, SIAM Journal on Optimization, 22 (2012), no. 3, 1065-1088.
12 Y.B. Zhao, An approximation theory of matrix rank minimization and its application to quadratic equations, Linear Algebra and Its Applications, 437 (2012), no. 1, 77--93.
13. Y.B. Zhao, Convexity condition and Legendre-Fenchel transform of the product of finitely many quadratic forms, Applied Mathematics and Optimization, 62 (2010), no.3, 411--434.
14. Y.B. Zhao, The Legendre-Fenchel conjugate of the product of two positive-definite quadratic forms, SIAM Journal on Matrix Analysis and Applications, 31 (2010). no. 4, 1792-1811.
15. I. Averbakh and Y.B. Zhao, Explicit reformulation for robust mathematical optimization with general uncertainty sets, SIAM Journal on Optimization, 18 (2008), no. 4, 1436--1466.
16. Y.B. Zhao, S.C. Fang and D. Li, Constructing generalized mean functions using convex functions with regularity, SIAM Journal On Optimization, 17 (2006), no. 1, 37-51.
17. J. Peng, T. Terlaky and Y.B. Zhao, A predictor-corrector algorithm for linear optimization based on a specific self-regular proximity function. SIAM Journal on Optimization, 15 (2005), no. 4, 1105--1127.
18. Y.B. Zhao and D. Li, A globally and locally superlinearly convergent non-interior-point algorithm for P0 LCPs, SIAM Journal on Optimization, 13 (2003), no. 4, 1195-1221.
19. Y.B. Zhao and D. Li, Locating the least 2-norm solution of linear programs via a path-following method, SIAM Journal on Optimization, 12 (2002), no. 4, 893-912.
20. Y.B. Zhao and D. Li, Existence and limiting behavior of a non-interior-point trajectory for nonlinear complementarity problems without strict feasibility condition. SIAM Journal on Control and Optimization, 40 (2001), no. 3, 898-924.
21. Y.B. Zhao and D. Li, On a new homotopy continuation trajectory for nonlinear complementary problems. Mathematics of Operations Research, 26 (2001), no. 1, 119--146
22. Y.B. Zhao and D. Li, Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM Journal on Optimization, 11 (2001), no. 4, 962--973.
23. Y.B. Zhao and G. Isac, Properties of a multi-valued mapping associated with some non-monotone complementarity problems. SIAM Journal on Control and Optimization, 39 (2000), no. 2, 571-593.
POSITION/TITLE
Research Scientist, Associate Researcher
RESEARCH FIELD
Tensor optimization, Manifold optimization, Signal processing
EMIAL
lijianze@gma
EDUCATION BACKGROUND
PhD (Chern Institute of Mathematics, Nankai University,)
BS (Hebei University of Technology)
BIOGRAPHY
Jianze Li has been working at the Shenzhen Research Institute of Big Data (SRIBD) since May 2019, as a Research Scientist and Associate Researcher. Before this, he graduated from the Chern Institute of Mathematics of Nankai University in June 2013 with a doctorate; from July 2013 to January 2018, he worked as a Lecturer at the School of Mathematics of Tianjin University; from September 2016 to August 2018, he conducted academic visits and postdoctoral research in GIPSA-Lab of Université Grenoble Alpes, France; from September 2018 to April 2019, he conducted academic visits at Ryerson University in Canada. He has published more than 10 scientific research papers in SIMAX, SIOPT, JMAA and other SCI journals, and completed a NSFC project (Youth Program) and a SRIBD project. He is currently the PI of a research project funded by the Guangdong Basic and Applied Basic Research Foundation (General Program).
ACADEMIC PUBLICATIONS
[1] Wentao Ding, Jianze Li, Shuzhong Zhang, Projectively and weakly simultaneously diagonalizable matrices and their applications, arXiv:2205.13245, accepted by SIAM Journal on Matrix Analysis and Applications, 2023.
[2] Jianze Li, Shuzhong Zhang, Polar decomposition-based algorithms on the product of Stiefel manifolds with applications in tensor approximation. Journal of the Operations Research Society of China, 2023. https://doi.org/10.1007/s40305-023-00462-8
[3] Jianze Li, Konstantin Usevich, Pierre Comon, Convergence of Gradient-Based Block Coordinate Descent Algorithms for Nonorthogonal Joint Approximate Diagonalization of Matrices, SIAM Journal on Matrix Analysis and Applications, 44(2), 592-621, 2023.
[4] Zhou Sheng, Jianze Li, Qin Ni, Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations. Mathematics of Computation, 92(343), 2217-2245, 2023.
[5] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Data-Free Backdoor Removal Based on Channel Lipschitzness, ECCV 2022.
[6] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Pre-activation Distributions Expose Backdoor Neurons, NeurIPS 2022.
[7] Jianze Li, Konstantin Usevich, Pierre Comon, Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis. Pacific Journal of Optimization, 17(3), 357–379, 2021.
[8] Konstantin Usevich, Jianze Li, Pierre Comon, Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms. SIAM Journal on Optimization, 30(4), 2998–3028, 2020.
[9] Jianze Li, Konstantin Usevich, Pierre Comon, On the convergence of Jacobi-type algorithms for Independent Component Analysis. Proc. IEEE SAM, 2020.
[10] Jianze Li, Konstantin Usevich, Pierre Comon, On approximate diagonalization of third order symmetric tensors by orthogonal transformations. Linear Algebra and its Applications, 576, 324-351, 2019.
[11] Jianze Li, Konstantin Usevich, Pierre Comon, Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization. SIAM Journal on Matrix Analysis and Applications, 39(1), 1-22, 2018.
[12] Jianze Li, Xiao-Ping Zhang, Tuan Tran, Point cloud denoising based on tensor Tucker decomposition. Proc. IEEE ICIP, 2019.
[13] Guang-Gui Ding, Jianze Li. Isometric extension problem between strictly convex two-dimensional normed spaces. Acta Mathematica Sinica, English Series, 35(4), 513-518, 2019.
[14] Jianze Li, Mazur–Ulam property of the sum of two strictly convex Banach spaces. Bulletin of the Australian Mathematical Society, 93(3), 473-485, 2016.
[15] Guang-Gui Ding, Jianze Li, Isometries between unit spheres of the l^\infty-sum of strictly convex normed spaces. Bulletin of the Australian Mathematical Society, 88(3), 369-375, 2013.
[16] Guang-Gui Ding, Jianze Li, Sharp corner points and isometric extension problem in Banach spaces. Journal of Mathematical Analysis and Applications, 405(1), 297-309, 2013.
[17] Jianze Li, Chi-Keung Ng, Tensor products for non-unital operator systems. Journal of Mathematical Analysis and Applications, 396(2), 601-605, 2012.
[18] Jianze Li, Possibly non-unital operator system structures on a possibly non-unital function system. Frontiers of Mathematics in China, 7(5), 847-855, 2012.
POSITION/TITLE
Research Scientist, Associate Researcher
RESEARCH FIELD
Tensor optimization, Manifold optimization, Signal processing
EMIAL
lijianze@gma
EDUCATION BACKGROUND
PhD (Chern Institute of Mathematics, Nankai University,)
BS (Hebei University of Technology)
BIOGRAPHY
Jianze Li has been working at the Shenzhen Research Institute of Big Data (SRIBD) since May 2019, as a Research Scientist and Associate Researcher. Before this, he graduated from the Chern Institute of Mathematics of Nankai University in June 2013 with a doctorate; from July 2013 to January 2018, he worked as a Lecturer at the School of Mathematics of Tianjin University; from September 2016 to August 2018, he conducted academic visits and postdoctoral research in GIPSA-Lab of Université Grenoble Alpes, France; from September 2018 to April 2019, he conducted academic visits at Ryerson University in Canada. He has published more than 10 scientific research papers in SIMAX, SIOPT, JMAA and other SCI journals, and completed a NSFC project (Youth Program) and a SRIBD project. He is currently the PI of a research project funded by the Guangdong Basic and Applied Basic Research Foundation (General Program).
ACADEMIC PUBLICATIONS
[1] Wentao Ding, Jianze Li, Shuzhong Zhang, Projectively and weakly simultaneously diagonalizable matrices and their applications, arXiv:2205.13245, accepted by SIAM Journal on Matrix Analysis and Applications, 2023.
[2] Jianze Li, Shuzhong Zhang, Polar decomposition-based algorithms on the product of Stiefel manifolds with applications in tensor approximation. Journal of the Operations Research Society of China, 2023. https://doi.org/10.1007/s40305-023-00462-8
[3] Jianze Li, Konstantin Usevich, Pierre Comon, Convergence of Gradient-Based Block Coordinate Descent Algorithms for Nonorthogonal Joint Approximate Diagonalization of Matrices, SIAM Journal on Matrix Analysis and Applications, 44(2), 592-621, 2023.
[4] Zhou Sheng, Jianze Li, Qin Ni, Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations. Mathematics of Computation, 92(343), 2217-2245, 2023.
[5] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Data-Free Backdoor Removal Based on Channel Lipschitzness, ECCV 2022.
[6] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Pre-activation Distributions Expose Backdoor Neurons, NeurIPS 2022.
[7] Jianze Li, Konstantin Usevich, Pierre Comon, Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis. Pacific Journal of Optimization, 17(3), 357–379, 2021.
[8] Konstantin Usevich, Jianze Li, Pierre Comon, Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms. SIAM Journal on Optimization, 30(4), 2998–3028, 2020.
[9] Jianze Li, Konstantin Usevich, Pierre Comon, On the convergence of Jacobi-type algorithms for Independent Component Analysis. Proc. IEEE SAM, 2020.
[10] Jianze Li, Konstantin Usevich, Pierre Comon, On approximate diagonalization of third order symmetric tensors by orthogonal transformations. Linear Algebra and its Applications, 576, 324-351, 2019.
[11] Jianze Li, Konstantin Usevich, Pierre Comon, Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization. SIAM Journal on Matrix Analysis and Applications, 39(1), 1-22, 2018.
[12] Jianze Li, Xiao-Ping Zhang, Tuan Tran, Point cloud denoising based on tensor Tucker decomposition. Proc. IEEE ICIP, 2019.
[13] Guang-Gui Ding, Jianze Li. Isometric extension problem between strictly convex two-dimensional normed spaces. Acta Mathematica Sinica, English Series, 35(4), 513-518, 2019.
[14] Jianze Li, Mazur–Ulam property of the sum of two strictly convex Banach spaces. Bulletin of the Australian Mathematical Society, 93(3), 473-485, 2016.
[15] Guang-Gui Ding, Jianze Li, Isometries between unit spheres of the l^\infty-sum of strictly convex normed spaces. Bulletin of the Australian Mathematical Society, 88(3), 369-375, 2013.
[16] Guang-Gui Ding, Jianze Li, Sharp corner points and isometric extension problem in Banach spaces. Journal of Mathematical Analysis and Applications, 405(1), 297-309, 2013.
[17] Jianze Li, Chi-Keung Ng, Tensor products for non-unital operator systems. Journal of Mathematical Analysis and Applications, 396(2), 601-605, 2012.
[18] Jianze Li, Possibly non-unital operator system structures on a possibly non-unital function system. Frontiers of Mathematics in China, 7(5), 847-855, 2012.
POSITION/TITLE
Research Scientist, Associate Researcher
RESEARCH FIELD
Tensor optimization, Manifold optimization, Signal processing
EMIAL
lijianze@gma
EDUCATION BACKGROUND
PhD (Chern Institute of Mathematics, Nankai University,)
BS (Hebei University of Technology)
BIOGRAPHY
Jianze Li has been working at the Shenzhen Research Institute of Big Data (SRIBD) since May 2019, as a Research Scientist and Associate Researcher. Before this, he graduated from the Chern Institute of Mathematics of Nankai University in June 2013 with a doctorate; from July 2013 to January 2018, he worked as a Lecturer at the School of Mathematics of Tianjin University; from September 2016 to August 2018, he conducted academic visits and postdoctoral research in GIPSA-Lab of Université Grenoble Alpes, France; from September 2018 to April 2019, he conducted academic visits at Ryerson University in Canada. He has published more than 10 scientific research papers in SIMAX, SIOPT, JMAA and other SCI journals, and completed a NSFC project (Youth Program) and a SRIBD project. He is currently the PI of a research project funded by the Guangdong Basic and Applied Basic Research Foundation (General Program).
ACADEMIC PUBLICATIONS
[1] Wentao Ding, Jianze Li, Shuzhong Zhang, Projectively and weakly simultaneously diagonalizable matrices and their applications, arXiv:2205.13245, accepted by SIAM Journal on Matrix Analysis and Applications, 2023.
[2] Jianze Li, Shuzhong Zhang, Polar decomposition-based algorithms on the product of Stiefel manifolds with applications in tensor approximation. Journal of the Operations Research Society of China, 2023. https://doi.org/10.1007/s40305-023-00462-8
[3] Jianze Li, Konstantin Usevich, Pierre Comon, Convergence of Gradient-Based Block Coordinate Descent Algorithms for Nonorthogonal Joint Approximate Diagonalization of Matrices, SIAM Journal on Matrix Analysis and Applications, 44(2), 592-621, 2023.
[4] Zhou Sheng, Jianze Li, Qin Ni, Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations. Mathematics of Computation, 92(343), 2217-2245, 2023.
[5] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Data-Free Backdoor Removal Based on Channel Lipschitzness, ECCV 2022.
[6] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Pre-activation Distributions Expose Backdoor Neurons, NeurIPS 2022.
[7] Jianze Li, Konstantin Usevich, Pierre Comon, Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis. Pacific Journal of Optimization, 17(3), 357–379, 2021.
[8] Konstantin Usevich, Jianze Li, Pierre Comon, Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms. SIAM Journal on Optimization, 30(4), 2998–3028, 2020.
[9] Jianze Li, Konstantin Usevich, Pierre Comon, On the convergence of Jacobi-type algorithms for Independent Component Analysis. Proc. IEEE SAM, 2020.
[10] Jianze Li, Konstantin Usevich, Pierre Comon, On approximate diagonalization of third order symmetric tensors by orthogonal transformations. Linear Algebra and its Applications, 576, 324-351, 2019.
[11] Jianze Li, Konstantin Usevich, Pierre Comon, Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization. SIAM Journal on Matrix Analysis and Applications, 39(1), 1-22, 2018.
[12] Jianze Li, Xiao-Ping Zhang, Tuan Tran, Point cloud denoising based on tensor Tucker decomposition. Proc. IEEE ICIP, 2019.
[13] Guang-Gui Ding, Jianze Li. Isometric extension problem between strictly convex two-dimensional normed spaces. Acta Mathematica Sinica, English Series, 35(4), 513-518, 2019.
[14] Jianze Li, Mazur–Ulam property of the sum of two strictly convex Banach spaces. Bulletin of the Australian Mathematical Society, 93(3), 473-485, 2016.
[15] Guang-Gui Ding, Jianze Li, Isometries between unit spheres of the l^\infty-sum of strictly convex normed spaces. Bulletin of the Australian Mathematical Society, 88(3), 369-375, 2013.
[16] Guang-Gui Ding, Jianze Li, Sharp corner points and isometric extension problem in Banach spaces. Journal of Mathematical Analysis and Applications, 405(1), 297-309, 2013.
[17] Jianze Li, Chi-Keung Ng, Tensor products for non-unital operator systems. Journal of Mathematical Analysis and Applications, 396(2), 601-605, 2012.
[18] Jianze Li, Possibly non-unital operator system structures on a possibly non-unital function system. Frontiers of Mathematics in China, 7(5), 847-855, 2012.
POSITION/TITLE
Research Scientist, Associate Researcher
RESEARCH FIELD
Tensor optimization, Manifold optimization, Signal processing
EMIAL
lijianze@gma
EDUCATION BACKGROUND
PhD (Chern Institute of Mathematics, Nankai University,)
BS (Hebei University of Technology)
BIOGRAPHY
Jianze Li has been working at the Shenzhen Research Institute of Big Data (SRIBD) since May 2019, as a Research Scientist and Associate Researcher. Before this, he graduated from the Chern Institute of Mathematics of Nankai University in June 2013 with a doctorate; from July 2013 to January 2018, he worked as a Lecturer at the School of Mathematics of Tianjin University; from September 2016 to August 2018, he conducted academic visits and postdoctoral research in GIPSA-Lab of Université Grenoble Alpes, France; from September 2018 to April 2019, he conducted academic visits at Ryerson University in Canada. He has published more than 10 scientific research papers in SIMAX, SIOPT, JMAA and other SCI journals, and completed a NSFC project (Youth Program) and a SRIBD project. He is currently the PI of a research project funded by the Guangdong Basic and Applied Basic Research Foundation (General Program).
ACADEMIC PUBLICATIONS
[1] Wentao Ding, Jianze Li, Shuzhong Zhang, Projectively and weakly simultaneously diagonalizable matrices and their applications, arXiv:2205.13245, accepted by SIAM Journal on Matrix Analysis and Applications, 2023.
[2] Jianze Li, Shuzhong Zhang, Polar decomposition-based algorithms on the product of Stiefel manifolds with applications in tensor approximation. Journal of the Operations Research Society of China, 2023. https://doi.org/10.1007/s40305-023-00462-8
[3] Jianze Li, Konstantin Usevich, Pierre Comon, Convergence of Gradient-Based Block Coordinate Descent Algorithms for Nonorthogonal Joint Approximate Diagonalization of Matrices, SIAM Journal on Matrix Analysis and Applications, 44(2), 592-621, 2023.
[4] Zhou Sheng, Jianze Li, Qin Ni, Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations. Mathematics of Computation, 92(343), 2217-2245, 2023.
[5] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Data-Free Backdoor Removal Based on Channel Lipschitzness, ECCV 2022.
[6] Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu, Pre-activation Distributions Expose Backdoor Neurons, NeurIPS 2022.
[7] Jianze Li, Konstantin Usevich, Pierre Comon, Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis. Pacific Journal of Optimization, 17(3), 357–379, 2021.
[8] Konstantin Usevich, Jianze Li, Pierre Comon, Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms. SIAM Journal on Optimization, 30(4), 2998–3028, 2020.
[9] Jianze Li, Konstantin Usevich, Pierre Comon, On the convergence of Jacobi-type algorithms for Independent Component Analysis. Proc. IEEE SAM, 2020.
[10] Jianze Li, Konstantin Usevich, Pierre Comon, On approximate diagonalization of third order symmetric tensors by orthogonal transformations. Linear Algebra and its Applications, 576, 324-351, 2019.
[11] Jianze Li, Konstantin Usevich, Pierre Comon, Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization. SIAM Journal on Matrix Analysis and Applications, 39(1), 1-22, 2018.
[12] Jianze Li, Xiao-Ping Zhang, Tuan Tran, Point cloud denoising based on tensor Tucker decomposition. Proc. IEEE ICIP, 2019.
[13] Guang-Gui Ding, Jianze Li. Isometric extension problem between strictly convex two-dimensional normed spaces. Acta Mathematica Sinica, English Series, 35(4), 513-518, 2019.
[14] Jianze Li, Mazur–Ulam property of the sum of two strictly convex Banach spaces. Bulletin of the Australian Mathematical Society, 93(3), 473-485, 2016.
[15] Guang-Gui Ding, Jianze Li, Isometries between unit spheres of the l^\infty-sum of strictly convex normed spaces. Bulletin of the Australian Mathematical Society, 88(3), 369-375, 2013.
[16] Guang-Gui Ding, Jianze Li, Sharp corner points and isometric extension problem in Banach spaces. Journal of Mathematical Analysis and Applications, 405(1), 297-309, 2013.
[17] Jianze Li, Chi-Keung Ng, Tensor products for non-unital operator systems. Journal of Mathematical Analysis and Applications, 396(2), 601-605, 2012.
[18] Jianze Li, Possibly non-unital operator system structures on a possibly non-unital function system. Frontiers of Mathematics in China, 7(5), 847-855, 2012.
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
Research Associate Professor at The Chinese University of Hong Kong, Shenzhen
RESEARCH FIELD
Artificial Intelligence, Machine Learning
EMIAL
wyli@cuhk.edu.cn
EDUCATION BACKGROUND
BSc. in Computer Science, Shandong University
MEng. in Computer Science, Chinese Academy of Sciences
Ph.D in Computer Science and Engineering, The Chinese University of Hong Kong
Major Achievements/Honors
1) Presidential Exemplar Teaching Award, CUHK-SZ, 2021.
2) Best Paper Finalist (short paper), ACM-CIKM’2021.
3) Best Paper Finalist, IEEE-ICAL’2011.
4) Best Paper in Logistics Award, IEEE-ICAL’2010.
BIOGRAPHY
Dr. Li conducted postdoctoral research at The Chinese University of Hong Kong and the University of Alberta, Canada from 2007 to 2009, and taught at the Macau Polytechnic Institute from 2009 to 2016. In August 2016, Dr. Li joined The Chinese University of Hong Kong (Shenzhen) and Shenzhen Institute of Big Data, engaged in teaching and research work in the field of computer and information science.
Dr. Li has published more than 40 first-author papers in the field of machine learning and artificial intelligence, and has completed 6 basic research projects funded by the government. He has served as the organizer and reviewer of many academic conferences such as NIPS, IJCAI, and AAAI.
ACADEMIC PUBLICATIONS
1) Li Wenye, Yu F., Ma Z. (2023) Metric Nearness Made Practical. In The 37th AAAI Conference on Artificial Intelligence (AAAI’2023).
2) Li Wenye, Yu F. (2022) Calibrating Distance Metrics Under Uncertainty. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
(ECML’2022).
3) Li Wenye (2020) Modeling Winner-Take-All Competition in Sparse Binary Projections. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML’2020).
4) Li Wenye, Mao J., Zhang Y., Cui S. (2018) Fast Similarity Search viaOptimal Sparse Lifting. In Advances in Neural Information Processing Systems 31 (NeurIPS’2018).
5) Li Wenye, Zhang J., Zhou J., Cui L. (2018) Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach. In 27th International Joint Conference on Artificial Intelligence (IJCAI’2018).
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
Research Associate Professor at The Chinese University of Hong Kong, Shenzhen
RESEARCH FIELD
Artificial Intelligence, Machine Learning
EMIAL
wyli@cuhk.edu.cn
EDUCATION BACKGROUND
BSc. in Computer Science, Shandong University
MEng. in Computer Science, Chinese Academy of Sciences
Ph.D in Computer Science and Engineering, The Chinese University of Hong Kong
Major Achievements/Honors
1) Presidential Exemplar Teaching Award, CUHK-SZ, 2021.
2) Best Paper Finalist (short paper), ACM-CIKM’2021.
3) Best Paper Finalist, IEEE-ICAL’2011.
4) Best Paper in Logistics Award, IEEE-ICAL’2010.
BIOGRAPHY
Dr. Li conducted postdoctoral research at The Chinese University of Hong Kong and the University of Alberta, Canada from 2007 to 2009, and taught at the Macau Polytechnic Institute from 2009 to 2016. In August 2016, Dr. Li joined The Chinese University of Hong Kong (Shenzhen) and Shenzhen Institute of Big Data, engaged in teaching and research work in the field of computer and information science.
Dr. Li has published more than 40 first-author papers in the field of machine learning and artificial intelligence, and has completed 6 basic research projects funded by the government. He has served as the organizer and reviewer of many academic conferences such as NIPS, IJCAI, and AAAI.
ACADEMIC PUBLICATIONS
1) Li Wenye, Yu F., Ma Z. (2023) Metric Nearness Made Practical. In The 37th AAAI Conference on Artificial Intelligence (AAAI’2023).
2) Li Wenye, Yu F. (2022) Calibrating Distance Metrics Under Uncertainty. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
(ECML’2022).
3) Li Wenye (2020) Modeling Winner-Take-All Competition in Sparse Binary Projections. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML’2020).
4) Li Wenye, Mao J., Zhang Y., Cui S. (2018) Fast Similarity Search viaOptimal Sparse Lifting. In Advances in Neural Information Processing Systems 31 (NeurIPS’2018).
5) Li Wenye, Zhang J., Zhou J., Cui L. (2018) Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach. In 27th International Joint Conference on Artificial Intelligence (IJCAI’2018).
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
Research Associate Professor at The Chinese University of Hong Kong, Shenzhen
RESEARCH FIELD
Artificial Intelligence, Machine Learning
EMIAL
wyli@cuhk.edu.cn
EDUCATION BACKGROUND
BSc. in Computer Science, Shandong University
MEng. in Computer Science, Chinese Academy of Sciences
Ph.D in Computer Science and Engineering, The Chinese University of Hong Kong
Major Achievements/Honors
1) Presidential Exemplar Teaching Award, CUHK-SZ, 2021.
2) Best Paper Finalist (short paper), ACM-CIKM’2021.
3) Best Paper Finalist, IEEE-ICAL’2011.
4) Best Paper in Logistics Award, IEEE-ICAL’2010.
BIOGRAPHY
Dr. Li conducted postdoctoral research at The Chinese University of Hong Kong and the University of Alberta, Canada from 2007 to 2009, and taught at the Macau Polytechnic Institute from 2009 to 2016. In August 2016, Dr. Li joined The Chinese University of Hong Kong (Shenzhen) and Shenzhen Institute of Big Data, engaged in teaching and research work in the field of computer and information science.
Dr. Li has published more than 40 first-author papers in the field of machine learning and artificial intelligence, and has completed 6 basic research projects funded by the government. He has served as the organizer and reviewer of many academic conferences such as NIPS, IJCAI, and AAAI.
ACADEMIC PUBLICATIONS
1) Li Wenye, Yu F., Ma Z. (2023) Metric Nearness Made Practical. In The 37th AAAI Conference on Artificial Intelligence (AAAI’2023).
2) Li Wenye, Yu F. (2022) Calibrating Distance Metrics Under Uncertainty. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
(ECML’2022).
3) Li Wenye (2020) Modeling Winner-Take-All Competition in Sparse Binary Projections. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML’2020).
4) Li Wenye, Mao J., Zhang Y., Cui S. (2018) Fast Similarity Search viaOptimal Sparse Lifting. In Advances in Neural Information Processing Systems 31 (NeurIPS’2018).
5) Li Wenye, Zhang J., Zhou J., Cui L. (2018) Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach. In 27th International Joint Conference on Artificial Intelligence (IJCAI’2018).
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
Research Associate Professor at The Chinese University of Hong Kong, Shenzhen
RESEARCH FIELD
Artificial Intelligence, Machine Learning
EMIAL
wyli@cuhk.edu.cn
EDUCATION BACKGROUND
BSc. in Computer Science, Shandong University
MEng. in Computer Science, Chinese Academy of Sciences
Ph.D in Computer Science and Engineering, The Chinese University of Hong Kong
Major Achievements/Honors
1) Presidential Exemplar Teaching Award, CUHK-SZ, 2021.
2) Best Paper Finalist (short paper), ACM-CIKM’2021.
3) Best Paper Finalist, IEEE-ICAL’2011.
4) Best Paper in Logistics Award, IEEE-ICAL’2010.
BIOGRAPHY
Dr. Li conducted postdoctoral research at The Chinese University of Hong Kong and the University of Alberta, Canada from 2007 to 2009, and taught at the Macau Polytechnic Institute from 2009 to 2016. In August 2016, Dr. Li joined The Chinese University of Hong Kong (Shenzhen) and Shenzhen Institute of Big Data, engaged in teaching and research work in the field of computer and information science.
Dr. Li has published more than 40 first-author papers in the field of machine learning and artificial intelligence, and has completed 6 basic research projects funded by the government. He has served as the organizer and reviewer of many academic conferences such as NIPS, IJCAI, and AAAI.
ACADEMIC PUBLICATIONS
1) Li Wenye, Yu F., Ma Z. (2023) Metric Nearness Made Practical. In The 37th AAAI Conference on Artificial Intelligence (AAAI’2023).
2) Li Wenye, Yu F. (2022) Calibrating Distance Metrics Under Uncertainty. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
(ECML’2022).
3) Li Wenye (2020) Modeling Winner-Take-All Competition in Sparse Binary Projections. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML’2020).
4) Li Wenye, Mao J., Zhang Y., Cui S. (2018) Fast Similarity Search viaOptimal Sparse Lifting. In Advances in Neural Information Processing Systems 31 (NeurIPS’2018).
5) Li Wenye, Zhang J., Zhou J., Cui L. (2018) Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach. In 27th International Joint Conference on Artificial Intelligence (IJCAI’2018).
POSITION/TITLE
Research Scientist of DFR
Assistant Professor of CUHKSZ
RESEARCH FIELD
Machine Learning, Computer Vision, Optimization, Statistical Process Control, Neural Signal Processing
fanjicong@cuhk.edu.cn
EDUCATION BACKGROUND
Ph.D. Electronic Engineering, City University of Hong Kong, 2013-2018
M.S. Control Science and Engineering, Beijing University of Chemical Technology, 2010-2013
B.S. Automation, Beijing University of Chemical Technology, 2006-2010
PERSONAL INTRODUCTION
Professor Jicong Fan is a Research Scientise of SRIBD, Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Professor Fan previously obtained his Ph.D. from the Department of Electronic Engineering, City University of Hong Kong in 2018, and his Master’s degree in Control Science and Engineering and Bachelor’s degree in Automation from Beijing University of Chemical Technology in 2013 and 2010 respectively. Prior to joining CUHK-Shenzhen, he was a postdoc associate at Cornell University. He held research positions at The University of Wisconsin-Madison and The University of Hong Kong in 2018 and 2015 respectively.
Professor Fan's research interests are Artificial Intelligence and Machine Learning. Particularly, he has done a lot of work on matrix/tensor methods, clustering algorithms, anomaly/outlier/fault detection, deep learning, and recommendation system. His research has been published on prestigious journals and conferences such as IEEE TSP/TNNLS/TII, KDD, NeurIPS, CVPR, ICLR, and AAAI. He is a senior member of IEEE and is serving as an associate editor for the journal Neural Processing Letters.
ACADEMIC PUBLICATIONS
[1] Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell. Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. Transactions on Machine Learning Research. 2023.01.
[2] Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang. A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. (acceptance rate=25.6%)
[3] Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022.
[4] Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. (acceptance rate=32.3%)
[5] Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%)
[6] Jicong Fan, Chengrun Yang, Madeleine Udell. Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE Transactions on Signal Processing, 2021(69): 1755-1770.
[7] Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell. Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%)
[8] Jicong Fan, Madeleine Udell. Online high-rank matrix completion. CVPR 2019. Oral Presentation. (acceptance rate=5.6%)
POSITION/TITLE
Research Scientist of DFR
Assistant Professor of CUHKSZ
RESEARCH FIELD
Machine Learning, Computer Vision, Optimization, Statistical Process Control, Neural Signal Processing
fanjicong@cuhk.edu.cn
EDUCATION BACKGROUND
Ph.D. Electronic Engineering, City University of Hong Kong, 2013-2018
M.S. Control Science and Engineering, Beijing University of Chemical Technology, 2010-2013
B.S. Automation, Beijing University of Chemical Technology, 2006-2010
PERSONAL INTRODUCTION
Professor Jicong Fan is a Research Scientise of SRIBD, Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Professor Fan previously obtained his Ph.D. from the Department of Electronic Engineering, City University of Hong Kong in 2018, and his Master’s degree in Control Science and Engineering and Bachelor’s degree in Automation from Beijing University of Chemical Technology in 2013 and 2010 respectively. Prior to joining CUHK-Shenzhen, he was a postdoc associate at Cornell University. He held research positions at The University of Wisconsin-Madison and The University of Hong Kong in 2018 and 2015 respectively.
Professor Fan's research interests are Artificial Intelligence and Machine Learning. Particularly, he has done a lot of work on matrix/tensor methods, clustering algorithms, anomaly/outlier/fault detection, deep learning, and recommendation system. His research has been published on prestigious journals and conferences such as IEEE TSP/TNNLS/TII, KDD, NeurIPS, CVPR, ICLR, and AAAI. He is a senior member of IEEE and is serving as an associate editor for the journal Neural Processing Letters.
ACADEMIC PUBLICATIONS
[1] Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell. Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. Transactions on Machine Learning Research. 2023.01.
[2] Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang. A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. (acceptance rate=25.6%)
[3] Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022.
[4] Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. (acceptance rate=32.3%)
[5] Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%)
[6] Jicong Fan, Chengrun Yang, Madeleine Udell. Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE Transactions on Signal Processing, 2021(69): 1755-1770.
[7] Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell. Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%)
[8] Jicong Fan, Madeleine Udell. Online high-rank matrix completion. CVPR 2019. Oral Presentation. (acceptance rate=5.6%)
POSITION/TITLE
Research Scientist of DFR
Assistant Professor of CUHKSZ
RESEARCH FIELD
Machine Learning, Computer Vision, Optimization, Statistical Process Control, Neural Signal Processing
fanjicong@cuhk.edu.cn
EDUCATION BACKGROUND
Ph.D. Electronic Engineering, City University of Hong Kong, 2013-2018
M.S. Control Science and Engineering, Beijing University of Chemical Technology, 2010-2013
B.S. Automation, Beijing University of Chemical Technology, 2006-2010
PERSONAL INTRODUCTION
Professor Jicong Fan is a Research Scientise of SRIBD, Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Professor Fan previously obtained his Ph.D. from the Department of Electronic Engineering, City University of Hong Kong in 2018, and his Master’s degree in Control Science and Engineering and Bachelor’s degree in Automation from Beijing University of Chemical Technology in 2013 and 2010 respectively. Prior to joining CUHK-Shenzhen, he was a postdoc associate at Cornell University. He held research positions at The University of Wisconsin-Madison and The University of Hong Kong in 2018 and 2015 respectively.
Professor Fan's research interests are Artificial Intelligence and Machine Learning. Particularly, he has done a lot of work on matrix/tensor methods, clustering algorithms, anomaly/outlier/fault detection, deep learning, and recommendation system. His research has been published on prestigious journals and conferences such as IEEE TSP/TNNLS/TII, KDD, NeurIPS, CVPR, ICLR, and AAAI. He is a senior member of IEEE and is serving as an associate editor for the journal Neural Processing Letters.
ACADEMIC PUBLICATIONS
[1] Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell. Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. Transactions on Machine Learning Research. 2023.01.
[2] Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang. A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. (acceptance rate=25.6%)
[3] Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022.
[4] Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. (acceptance rate=32.3%)
[5] Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%)
[6] Jicong Fan, Chengrun Yang, Madeleine Udell. Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE Transactions on Signal Processing, 2021(69): 1755-1770.
[7] Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell. Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%)
[8] Jicong Fan, Madeleine Udell. Online high-rank matrix completion. CVPR 2019. Oral Presentation. (acceptance rate=5.6%)
