July 2017--Present. Research Associate Professor. School of Data and Computer Science, Sun Yat-sen University. Guangzhou, Guangdong, P. R. China.
October 2018-December 2018. Visiting Faculty. Department of Mathematics and Statistics, Old Dominion University. Norfolk, Virginia, USA.
October 2015--October 2016. Research Assistant. Department of Mathematical and Statistical Sciences, University of Alberta. Edmonton, Alberta, Canada.
September 2014--June 2017. Ph.D. in Computational Mathematics. School of Mathematics, Sun Yat-sen University. Guangzhou, Guangdong, P. R. China
1. Jan 2018-Dec 2020, PI, Fundamental Research Funds for the Central Universities, 120,000 RMB.
8. Rongrong Lin, Guohui Song, Haizhang Zhang*, Multi-task Learning in Vector-valued RKBSs with the l1 Norm, 24 Pages.
7. Rongrong Lin, Haizhang Zhang*, Reproducing Kernels with Uniformly Bounded Lebesgue Constants, Under Review, 3 Pages.
5. Rongrong Lin*, An Optimal Convergence Rate for Gaussian Regularized Shannon Sampling Series, Numerical Functional Analysis and Optimization, Accepted, 14 Pages. [PDF]
4. Rongrong Lin, Haizhang Zhang*, Convergence Analysis of the Gaussian Regularized Shannon Sampling Series, Numerical Functional Analysis and Optimization, 38 (2017), no. 2,224–247. [PDF]
2. Rongrong Lin, Haizhang Zhang*, Existence of the Bedrosian Identity for Fourier Multiplier Operators, Forum Mathematicum, 28 (2016), no. 4, 749–759. [PDF]
1. Wei Hu, Rongrong Lin*, Haizhang Zhang, The Circular Bedrosian Identity for Translation-Invariant Operators: Existence and Characterization, Mathematical Methods in the Applied Sciences, 38 (2015), no.18, 5264–5270. [PDF]
3. Contributed talk, 20 mins. The 2nd International Conference on Mathematics of Data Science. Old Dominion University, USA. November 3-4, 2018. (Topic: Multi-task Learning in Vector-valued RKBSs with the l1 norm)
2. Invited talk, 25 mins. The 2nd International Conference on Kernel-based Approximation Methods in Data Analysis. Guangzhou. May 25-27, 2018. (Topic: Constructions of Reproducing Kernel Banach Spaces) [link]
1. Invited talk, 40 mins. TSMIF Sanya workshop: From Approximation Theory to Real World Applications. TSMIF, Sanya. December 11-15, 2017. (Topic: Shannon's Sampling Theorem)
2017.09-2018.01 Wavelet Analysis.
Lecture 1: Introduction to DCT- and DWT-based JPEG
Lecture 2: Regularity of a Function and Decay of its Fourier Coefficients
Lecture 3: DFT, FFT and DCT
Lecture 4: Fourier transform on L1(R) and L2(R)
Lecture 5: Approximation Identity and Shannon's Sampling Theorem
Lecture 6: Celebrated Results in Fourier Analysis
Lecture 7: Wavelet Analysis: Haar Wavelet
Lecture 8: General Multiresolution Analysis and the Mallat Algorithm
Lecture 9: Filter Banks (Symmetry, Vanishing Moments, Sum Rules and Linear-phase Moments)
Lecture 10: Daubechies' Orthogonal Wavelets
Lecture 11: Biorthogonal Wavelets and Discrete Wavelet Transform
March 8-July 5, 2018. Mathematical Foundations of Deep Learning
Lecture 4: Recurrent Neural Networks (RNN) and LSTM
Lecture 7: Generative Adversarial Networks (GAN)
Lecture 8: Sparse Coding and Auto-Encoder (Speaker: Ganzhao Yuan)
Lecture 9: Object Detection (R-CNN, YOLO)
Lecture 10: Kernel Methods and Deep Networks I (RBF Network, DKL, VC Dimension)
Lecture 12: Kernel Methods and Deep Networks II (CKN, Overparametrization)
Lecture 13: Mathematical Foundations of Deep Learning (Approximation Properties)