Invited speakers, extensive literature study, students' research, collaborative study and discussion of book chapters/tutorials.
2020 GMSC Spring Meeting
Tentative Schedule (the schedule may be changed) Meetings have been postponed to Fall 2020
| Category | Time | Title | Speaker |
|---|---|---|---|
| [Talk] | Jan 31 2:00-3:15pm | The Self-Excited Black-Scholes Model | Dr. Alec Kercheval |
| [Talk] | Feb 21 2:00-3:15pm | Momentum Acceleration Under Random Gradient Noise | Dr. Lingjiong Zhu |
| [Tutorial] | Feb 28 2:00-3:15pm | Optimal Transport and Wasserstien Distance | Jiahui Shen |
| [Book Study] | Mar 6 2:00-3:15pm | Sparse Principal Component Analysis | Jiuwu Jin |
| [Presentation] | Mar 27 2:00-3:15pm | U-Statistics | Peng Zhao |
2019 GMSC Fall Meeting
| Category | Time | Title | Speaker |
|---|---|---|---|
| [Talk] | Sept 13 3:00-4pm (OSB 327) | Statistical Learning Methods for Heterogeneous Medical Imaging Data | Dr. Chao Huang |
| [Talk] | Sept 20 3:30-5pm | More than Space-time: Research in Multivariate/Multiscale Spatio-Temporal Statistics | Dr. Jonathan Bradley |
| [Presentation] | Sept 27 3:30-5pm | Functional Data Analysis with Application to Neuroimaging Data | Ian Xu |
| [Presentation] | Oct 11 4:30-6pm | Word Embedding and Data Visualization | Jiahui Shen |
| [Presentation] | Oct 18 3:30-5pm | A Generalized Framework of Optimal Two-stage Designs for Exploratory Basket Trials | Xiaoqiang Wu |
| [Book Study] | Nov 1 3:30-5pm | Random Matrix Theory and it's Applications | Jingze Liu |
| [Tutorial] | Nov 8 3:30-5pm | Undirected Graphical Models | Jiuwu Jin |
| [Book Study] | Nov 15 3:30-5pm | Sparse Recovery with Random Matrix Theory | Jiahui Shen |
| [Book Study] | Nov 22 3:30-5pm | Introduction to Kernel Methods | Pranay Tarafdar |
2019 GMSC Spring Meeting
| Time | Title | Speaker |
|---|---|---|
| Jan 25 1:30-3pm | Multiple Imputation Techniques for Large Data sets with Linear Constraints | Jian Cao, Dr. Paul Beaumont |
| Feb 1 1:30-3pm | Models for Matched Pairs and Data Ranking | Shaokang Ren |
| Feb 8 1:30-3pm | Models for Clustered Data | Peng Zhao |
| Feb 22 1:30-3pm | Instrumental Variables: Two Stage Least Squares | Jiahui Shen |
| Mar 8 1:30-3pm | Instrumental Variables in Practice, Cointegration and Error Correction Model | Dongrui Zhong |
| Mar 15 1:30-3pm | Model Aggregation | Xiaoqiang Wu |
| Apr 5 1:30-3pm | Gaussian Process and Poisson Process | Jiuwu Jin |
| Apr 19 1:30-3pm | Causal Inference | Wenhao Zhang |
2018 GMSC Fall Meeting
| Time | Title | Speaker |
|---|---|---|
| Sep 7 3:30-5pm | Gene-based Tests for Genome-wide Association Studies | Chong Wu |
| Sept 21 3:30-5pm | Learning with Imbalanced Data | Dongrui Zhong |
| Sept 28 3:30-5pm | High Dimensional Inference | Peng Zhao |
| Oct 5 3:30-5pm | A/B Testing, Data Structure, SQL | Kai Deng, Wenhao Zhang |
| Oct 19 3:30-5pm | Recurrent Neural Network with Application in Natural Language Processing | Pranay Tarafdar |
| Oct 26 3:30-5pm | Greedy Algorithm Analysis | Jiahui Shen |
| Nov 2 3-4:30pm | Reinforcement Learning | Jingze Liu |
| Nov 9 3:30-5pm | Generative Adversarial Networks | Shaokang Ren |
| Nov 30 3:30-5pm | False discovery rate Hashing |
Peng Zhao Jiuwu Jin |
2018 GMSC Spring Meeting
| Time | Title | Speaker |
|---|---|---|
| Jan 19 2-3:30pm | On the statistical modeling of count data in high dimensions | Shao Tang |
| Jan 26 2-3:30pm | CNN architectures: introduction & go deeper | Zhisheng Zhong |
| Feb 2 2-3:30pm | CNN architectures: go wider & more information flow | Zhisheng Zhong |
| Feb 9 2-3:30pm | Tensor regression and classification in high dimensions | Dr. Xin Zhang |
| Feb 16 2-3:30pm | Mirror descent, boosting, I projection, matrix raking | Jiahui Shen |
| Feb 23 2-3:30pm | Multi-label classification | Wenchen Liu |
| Mar 2 2-3:30pm | Concentration inequalities | Lizhe Sun |
| Mar 8 3-4:30pm | Entropy Methods | Boning Yang |
| Mar 23 2-3:30pm | Complexity bounds in machine learning I | Pranay Tarafdar |
| Mar 30 2-3:30pm | Complexity bounds in machine learning II | Wenhao Zhang |
| Apr 13 2-3:30pm | Minimax Theory | Jiuwu Jin, Jingze Liu |
2017 GMSC Fall Meeting
| 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 |