Student Seminar@Stat MSU, Spring 2013


Time: 6:30PM---8:30PM on Thursday
Location: C405 Wells Hall (except as noted)

Food served at C405 Wells Hall.


In this "Big Data" age, as Hal Varian, chief economist at Google said, "the sexy job in the next 10 years will be statisticians". Here "statisticians" are not just referred to people in Statistics department, instead, they are generally "Data Scientists", who can apply modern statistical methods to do data analysis to get knowledge from the massive data from various areas. Nowadays, Machine Learning, Graphical Models, Computational Bayesian Analysis and other methods are becoming more and more popular among "Data Scientists", with applications in Engineering, Computational Biology, Finance, Social Science and so on. And it's also easy for us to get real data online such as Kaggle, which is a famous website for data analysis competitions, whose data are provided by many large companies such as Facebook.

One hot area in statistics, high-dimensional data analysis deals with estimation in the "large p, small n" setting, where p and n correspond, respectively, to the dimensionality of the data and the sample size. It is well-known that such high-dimensional scaling can lead to dramatic breakdowns in many classical procedures. In the absence of additional model assumptions, it is frequently impossible to obtain consistent procedures when p is far bigger than n. Accordingly, an active line of statistical research is based on imposing various restrictions on the model, for instance, sparsity, manifold structure, or graphical model structure, and then studying the scaling behavior of different estimators as a function of sample size n, ambient dimension p and additional parameters related to these structural assumptions. So we can see that sparsity is not the only case for studying high dimensional statistics, and graphical model structure is also one of the reasonable assumptions. Thus learning graphical model is important not only for itself but also for high dimensional statistics.

In this student seminar, we are going to focus on Graphical Models, which is very popular among data scientists from companies and also among statisticians from academia. It involves Machine Learning, Bayesian Analysis, High-Dimensional Statistics and other hot areas in data analysis. We start with basics of the graphical models and related algorithms, probably for the whole fall semester, and then we move on to the high dimensional case in next spring semester. Along with the theory learning, we also try to implement the algorithms to realize the powerful theory in real data analysis, hence enhancing our computational and programming ability. This is an effective way to learn statistical methods.

In addition, in our department, Professor R.V.Ramamoorthi is working on Graphical Models, Professor Yuehua Cui is interested in the gene regulatory networks (GRN), which applies graphical models in the applications to genetics, Professor Ping-Shou Zhong is working on high dimensional inference, Professor Lifeng Wang is also interested in the graph-valued regression analysis, Professor Yimin Xiao is working on Random Fields, which includes Markov random fields (Undirected graphical models), Professor Sarat Dass, Tapabrata Maiti and Chae Young Lim are working on spatial data analysis and Bayesian analysis, which are closely related with Graphical Models. Thus, we really have excellent academic resources in our department to study Graphical Models. It can help us to cooperate with our Professors easily. And this can also help new students to choose academic advisors.

The student seminar is also a platform to strengthen students' presentation ability and stimulate the cooperation among students. Hopefully, we will end up with getting papers published at the end of the seminar.


01/17, Thur

Graphical Models via Generalized Linear Models (I)

Xin Qi
01/24, Thur
Sparse Matrix Graphical Models (I)
Yuzhen Zhou
01/31, Thur
Latent Variable Graphical Model Selection via Convex Optimization (I)
Honglang Wang
02/07, Thur
Generalized Hyper Markov Laws for DAG (I)
Xiaoqing Zhu
02/14, Thur
Learning High-dim DAG with Latent and Selection Variables
Bin Gao
02/21, Thur
Graphical Models via Generalized Linear Models (II)
Xin Qi
02/28, Thur
Sparse Matrix Graphical Models (II)
Yuzhen Zhou
03/14, Thur
Latent Variable Graphical Model Selection via Convex Optimization (II)
Honglang Wang
03/21, Thur
Generalized Hyper Markov Laws for DAG (II)
Xiaoqing Zhu
03/28, Thur
Discussing the topic for the fall semester in 2013
04/04, Thur
Abhishek Kaul
04/11, Thur
Abhishek Kaul
04/18, Thur
A Sparse Conditional Gaussian Graphical Model for Analysis of Genetical Genomics Data
Bin Gao

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