July 2017--Present, Research Associate Professor, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, P. R. China.
October 2018-December 2018, Visiting Scholar, Department of Mathematics and Statistics, Old Dominion University, Norfolk, USA.
October 2015--October 2016, Research Assistant, Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
September 2014--June 2017, Ph.D. in Computational Mathematics, School of Mathematics, Sun Yat-sen University, Guangzhou, P. R. China
Kernel Methods in Machine Learning; Time-Frequency Analysis
1. Jan 2018-Dec 2020, PI, Fundamental Research Funds for the Central Universities, 120,000 RMB.
7. Rongrong Lin, Guohui Song, Haizhang Zhang*, Multi-task Learning in Vector-valued RKBSs with the l1 Norm, 24 Pages, In preparation.
5. Rongrong Lin*, An Optimal Convergence Rate for Gaussian Regularized Shannon Sampling Series, Under Review, 14 Pages. [Paper]
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.
3. Fukeng Huang, Rongrong Lin*, Yunfei Yang, The Circular Bedrosian Identity and Multidimensional Periodic Analytic Signals, Complex Variables and Elliptic Equations, 62(2017), no. 2, 199–213.
2. Rongrong Lin, Haizhang Zhang*, Existence of the Bedrosian Identity for Fourier Multiplier Operators, Forum Mathematicum, 28 (2016), no. 4, 749–759.
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.
1. 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)
2. 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. An Introduction to 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)