2017 GMSC Fall Meeting (OSB 205)

invited speakers, extensive literature study, students' research, collaborative study and discussion of book chapters/tutorials.

Tentative Schedule (the order may be changed)

Category Time Title Speaker
[Talk] Sep 15  2-3:30pm Introduction to meta-analysis Dr. Lifeng Lin
[Talk] Sep 22  2-3:30pm Computational Models for Multimedia Pattern Recognition Dr. Shayok Chakraborty
[Talk] Sep 29  2-3:30pm Classification for cross-sectional and sequential data
Microbiome and Microarray Data Analysis
Zhifeng Wang, Hoang Tran
[Tutorial] Oct 6  2-3:30pm Advanced Newton-type Methods (code) Shao Tang
[Tutorial] Oct 13  2-3:30pm Cointegration (code) Hoang Tran
[Talk] Oct 20  2-3:30pm Deep Learning Applications Shao Tang
[Tutorial] Oct 27  2-3:30pm Boosting with applications in trees and networks Jiahui Shen
[Tutorial] Nov 3  2-3:30pm Approximate Message Passing Shao Tang
[Literature] Nov 9  2-3:30pm Randomized Algorithms Zhifeng Wang
[Tutorial] Nov 17  2-3:30pm Community Detection in Networks
Ranking in Statistics and Machine Learning
Boning Yang,
Lizhe Sun
[Tutorial] Dec 1  2-3:30pm Bandit Algorithms Zhifeng Wang

2017 GMSC Spring Meeting

The following two books had been used in the Spring of 2017:
1. Deep Learning (2016) by Ian Goodfellow, Yoshua Bengio, Aaron Courville
2. Computer Age Statistical Inference - Algorithms, Evidence, and Data Science (2016) by Bradley Efron and Trevor Hastie

Category Time Title Speaker
[Talk] Jan 13  2-3:30pm Holo-Spectrum Analysis: A Method for Quantifying Nonlinear Interactions Hidden in a Time Series Dr. Zhaohua Wu
[Presentation] Jan 20  2-3:30pm Indirect Gaussian Graph Learning beyond Gaussianity Shao Tang
[Presentation] Jan 27  2-3:30pm On the analysis of Bregman-surrogate algorithms for large-scale nonconvex optimization Zhifeng Wang
[Tutorial] Feb 3  2:30-4:00pm Introduction to SQL  (code) Zhifeng Wang
[Tutorial] Feb 10  2-3:30pm Large-Scale Hypothesis Testing and False-Discovery Rates Hoang Tran
[Talk] Feb 17  2-3:30pm A Short Introduction to the Analysis of Real Data Dr. Qing Mai
[Literature Study] Feb 24  2-3:30pm Stochastic Gradient Descent Shao Tang
[Book Study] Mar 3  2-3:30pm Introduction to Deep Learning Libo Wang, Liu Yang
[Tutorial] Mar 31  2-3:30pm Randomized Dimensionality Reduction Zhifeng Wang
[Tutorial] Apr 28  2:30-4pm Some Additional R Features  (RMarkdown) Hoang Tran

2016 GMSC Fall Meeting

The following two books had be partially covered in the Fall of 2016:
1. Statistical Learning with Sparsity - The Lasso and Generation (2015) by T. Hastie, R. Tibshirani and M. Wainwright
2. Sparse Modeling - Theory, Algorithms and Applications (2015) by I. Rish and G. Grabarnik

Category Time Title Speaker
[Talk] Sep. 9  2-3:30pm Fast Non-parametric Regression using Randomized Sketches Dr. Yun Yang
[Presentation] Sep. 16  2-3:30pm Iterative Proportional Scaling Shao Tang
[Book Study] Sep. 23  2-3:30pm Graphical Models Xin Sui, Shao Tang
[Talk] Sep. 30  3-4:30pm Data Science: A Personal View from the CS Perspective Dr. Peixiang Zhao
[Tutorial] Oct. 7  1:30-3:30pm Python Programming for Statisticians  (code) Zhifeng Wang
[Presentation] Oct. 14  3-4:30pm Discovery of Stock Chart Patterns by Kernel Smoothing and Automatic Outlier Detection Hoang Tran
[Book Study] Oct. 21  3:40-5:10pm Statistical Inference in High Dimensions Liu Yang, Libo Wang
[Tutorial] Oct. 28  3:30-5pm Deep Learning and its Applications Xin Sui
[Book Study] Nov. 4  3-4:30pm Sparse Matrix Factorization and Multivariate Methods Hoang Tran, Zhifeng Wang
[Talk] Dec. 2  2-3:30pm Volatility Estimation with High Frequency Financial Data Dr. Minjing Tao