Blog Archives

Assistant Professor of Statistics at IUPUI

August 21, 2015
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Assistant Professor of Statistics at IUPUI

I graduated from MSU this summer and moved to Indianapolis as an assistant professor of Statistics at IUPUI. This is my first official job in life. Welcome to my Homepage: math.iupui.edu/~hlwang !

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Useful for referring—02-20-2015

February 20, 2015
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Useful for referring—02-20-2015

  Deep Learning Master Class Advances in Variational Inference Numerical Optimization: Understanding L-BFGS An exact mapping between the Variational Renormalization Group and Deep Learning New ASA Guidelines for Undergraduate Statistics Programs 奇异值分解(We Recommend a Singular Value Decomposition) 如何简单形象又有趣地讲解神经网络是什么? Academic vs. Industry Careers Hadley Wickham: Impact the world by being useful Statisticians in World War II: […]

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Optimal Design of Experiments

January 11, 2015
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Optimal Design of Experiments

The first colloquium speaker at this semester, professor Wei Zheng from IUPUI, will give a talk on “Universally optimal designs for two interference models“. In this data explosive age, people are easy to get big data set, which renders people difficult to make inferences from such massive data. Since people usually think that with more […]

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Qualities Characterizing a Great PhD Student Given by David Karger from MIT

December 31, 2014
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Qualities Characterizing a Great PhD Student Given by David Karger from MIT

I am graduating as a fifth year PhD student and I really agree with Professor David Karger from MIT about the qualities characterizing a great PhD student, especially about the point on “discipline and productivity”. Professor Karger also distinguished the difference between a successful PhD for industry and a successful PhD for academic. Here I just […]

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Machine Learning Books Suggested by Michael I. Jordan from Berkeley

December 30, 2014
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Machine Learning Books Suggested by Michael I. Jordan from Berkeley

There has been a Machine Learning (ML) reading list of books in hacker news for a while, where Professor Michael I. Jordan recommend some books to start on ML for people who are going to devote many decades of their lives to the field, and who want to get to the research frontier fairly quickly. […]

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Functional Principal Component Analysis

December 20, 2014
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Functional Principal Component Analysis

In mathematics, a general principle for studying an object is always from the study of the object itself to the study of the relationship between objects. In functional data analysis, the most important part for studying of the object itself, i.e. one functional data set, is functional principal component analysis (FPCA). And for the study […]

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Degrees of Freedom and Information Criteria

December 4, 2014
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Degrees of Freedom and Information Criteria

Degrees of freedom and information criteria are two fundamental concepts in statistical modeling, which are also taught in introductory statistics courses. But what are the exact abstract definitions for them which can be used to derive specific calculation formula in different situations. I often use fit criteria like AIC and BIC to choose between models. […]

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Useful for referring—12-04-2014

December 4, 2014
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Useful for referring—12-04-2014

  Tutorial: How to detect spurious correlations, and how to find the … Practical illustration of Map-Reduce (Hadoop-style), on real data Jackknife logistic and linear regression for clustering and predict… From the trenches: 360-degrees data science A synthetic variance designed for Hadoop and big data Fast Combinatorial Feature Selection with New Definition of Predict… A […]

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Factor Analysis vs Principal Component Analysis

November 22, 2014
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Factor Analysis vs Principal Component Analysis

Recently some papers discussed in our journal club  are focused on integrative clustering of multiple omics data sets. I found that they are all originated from factor analysis and make use of the advantage of factor analysis over principal component analysis. Let’s recall the model for factor analysis: where () and , with mean and […]

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EM algorithm revisited

November 20, 2014
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EM algorithm revisited

On this Tuesday, Professor Xuming He presented their recent work on subgroup analysis, which is very interesting and useful in reality. Think about the following very much practical problem (since the drug is expensive or has certain amount of side effect): If you are given the drug response, some baseline covariates which have nothing to […]

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