## **2017 GMSC Fall Meeting** (OSB 205)

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

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**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 |