Picture of Linderman

George C. Linderman

General Surgery Resident
Department of Surgery
Massachusetts General Hospital
Email: glinderman[at]mgh.harvard.edu
Twitter: @GCLinderman

About me:

I completed my MD PhD training at Yale University, where I pursued a PhD in Applied Mathematics. Broadly speaking, my work has been in developing theory and methods for high dimensional data analysis.

At the present time, I am focusing on clinical training as a surgical resident at the Massachusetts General Hospital.

Software

I maintain the following software packages:

FIt-SNE: Fast Fourier Transform-accelerated Interpolation-based t-SNE
ALRA: Adaptively-thresholded Low Rank Approximation for imputation of scRNA-seq data
t-SNE Heatmaps: 1D t-SNE heatmaps to visualize expression patterns of hundreds of genes simultaneously in scRNA-seq
oocPCA: Out-of-Core Randomized Principal Component Analysis
pcafast: Randomized Principal Component Analysis

Teaching

High Dimensional Probability (Directed Reading Program Mentor, Fall 2018, Spring 2019, Fall 2019, Fall 2020)
MATH222: Linear Algebra with Applications (Teaching Fellow, Fall 2017)

Notes

Low Rank Approximation of Kernels: Intuition for the interpolation methods used to accelerate t-SNE
Concentration Inequalities: An introduction to concentration inequalities, with numerical demonstrations
Low-Rank Matrix Completion: An introduction to low-rank matrix completion

Selected publications

Papers where I was lead/co-lead author are listed below; see my Google Scholar page for complete list of publications.

Methods and Theory Papers

Initialization is critical for preserving global data structure in both t-SNE and UMAP (Nature Biotechnology, 2021)
Dimensionality Reduction of Single-Cell RNA-Seq Data (Methods in Molecular Biology, 2021)
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data (Nature Methods, 2019)
Zero-preserving imputation of scRNA-seq data using low rank approximation (bioRxiv, 2018)
Numerical Integration on Graphs: Where to Sample and How to Weigh (Mathematics of Computation, 2018, arXiv)
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science (Journal of Applied Probability, 2018, arXiv)
Clustering with t-SNE, Provably (SIAM Journal on Mathematics of Data Science, 2017)
Algorithm 971: An implementation of a randomized algorithm for principal component analysis (ACM Transactions of Mathematical Software, 2017)
MAGNET: MicroArray Gene expression and Evaluation Toolkit (Nucleic Acids Research, 2012)
BiC: a Web Server for Calculating Bimodality of Coexpression Between Gene and Protein Networks. (Bioinformatics, 2011)

Applications Papers

Improved outcomes using laparoscopy for emergency colectomy after mitigating bias by negative control exposure analysis (Surgery, 2021)
Increased mortality with REBOA only mitigated by strong unmeasured confounding: an expanded analysis using the National Trauma Data Bank (Journal of Trauma and Acute Care Surgery, 2021)
The Proximal Humeral Ossification System Improves Assessment of Maturity in Patients with Scoliosis (Journal of Joint and Bone Surgery, 2019)
Association of Body Mass Index With Blood Pressure Among 1.7 Million Chinese Adults (JAMA Network Open, 2018)