美国 佛罗里达 迈尔斯堡 联系方式
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I focus on the processing and analysis of biomedical and healthcare data.

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工作经历和教育背景

  • Blue Health Intelligence

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出版作品

  • GitHub Repository

    GitHub

    Repository of artifacts from various courses and projects.

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  • How To Make Slide Presentations in LaTeX: Using the beamer Class

    Tutorial I delivered for co-workers on how to create a slide presentation using LaTeX. Of course, this slide presentation itself was created using LaTeX!

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  • How To Make an R Package Based on C++ And Manage It With R-Forge: A Tutorial

    How to make an R package based on underlying C++ code. Also contains basic instructions for maintaining your R package using R-Forge.

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  • R package ptinpoly: Point-In-Polyhedron Test (3D)

    This library provides a function 'pip3d', which tests whether a point in 3D space is within, exactly on, or outside an enclosed surface defined by a triangular mesh.

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  • Non-negative Matrix Factorization: Assessing Methods for Evaluating the Number of Components, and the Effect of Normalization Thereon

    Master's Thesis

    Non-negative matrix factorization (NMF) is a relatively new method of matrix decomposition which factors an m-by-n data matrix X into an m-by-k matrix W and a k-by-n matrix H, so that X = W x H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components or latent "parts" into which X has been decomposed. The question arises: how does one choose k? In this thesis, we assess…

    Non-negative matrix factorization (NMF) is a relatively new method of matrix decomposition which factors an m-by-n data matrix X into an m-by-k matrix W and a k-by-n matrix H, so that X = W x H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components or latent "parts" into which X has been decomposed. The question arises: how does one choose k? In this thesis, we assess multiple methods for estimating the number of components k in the context of NMF, and we also examine the effects of various types of normalization on this estimate. We conclude that when estimating k, it is best not to perform any normalization. If it is known or assumed that the underlying components are orthogonal or nearly so, then perhaps Velicer's MAP or Minka's Laplace-PCA method might be best to use. However, in the general case where it is unknown whether the underlying components are orthogonal or not, none of the methods for estimating k seemed obviously better than the others.

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  • Tutorial: Getting Started With LaTeX

    Tutorial on getting started with LaTeX.

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  • Co-authored Peer-Reviewed Papers on PubMed

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    Papers on which I was a co-author, from my time N.I.H. as well as my time at Georgetown University. In these studies, I usually served as a data analysis / statistical consultant.

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  • Students' Corner columns (Washington Statistical Society newsletter, September 2007 - September 2008)

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    I served as the Student Representative to the Washington Statistical Society from September 2007 to September 2008. As part of my duties, I wrote a column for the monthly newsletter, entitled Students' Corner. Below are links to the columns that I wrote.

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荣誉奖项

  • Mu Sigma Rho

    Mu Sigma Rho Society

    National Statistics Honorary Society

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