Eric Bridgeford

Statistician & Project Management Expert

Eric Bridgeford

Statistician & Project Manager
  • email ericwb95 <-at->
  • place 3300 N Charles Street, Baltimore, MD

Hello! I'm Eric Bridgeford. I am a Ph.D. Student in the Department of Biostatistics at Johns Hopkins University. I am a Statistical Analyst focusing on independence testing, manifold embedding, and graph inference. I have a strong background in management and leadership of technical teams, and have a key interest in the integration of distributed computing and blockchain technologies into existing workflows.


Johns Hopkins University
B.S. in Biomedical Engineering and Computer Science
September 2013 - May 2017

I completed my undergraduate education at Johns Hopkins University, with my coursework focusing particularly at the intersection of statistics, bioengineering, and scalable computing.

Johns Hopkins Department of Biostatistics
Doctoral Student
September 2018 - present

I will be pursuing a Ph.D. in the Department of Biostatistics at Johns Hopkins University beginning this upcoming fall. My research will focus on graph inference with emphasis on mesoscale connectomics applications. I will be supervised by Dr. Brian Caffo, Dr. Joshua Vogelstein, and Dr. Carey Priebe.

demoSelected Skills

Package Development
Dimensionality Reduction
Graph Inference
Statistical Theory
Object-Oriented Programming
Scalable Computing
Product Delivery

0%: Beginner, 50%: Proficient, 100%: Expert


Undergraduate Researcher
University of Pennsylvania
May 2014 - February 2016

With Dr. Bassett and Dr. Muldoon, I aided in the development of a novel network statistic, the Small World Propensity. Our statistic provides an extension of small worldness to weighted graphs, a common element of real-world networks, and robustness to changes in edge density.

Undergraduate Researcher
October 2014 - May 2017

With the NeuroData Team, I worked with Dr. Vogelstein to develop statistics for quantifying reliability in multi-scan scientific data and a processing pipeline for functional MRI connectomics to extend our existing pipeline for diffusion connectomics.

Research Scientist
Johns Hopkins BME Department
September 2017 - present

With Dr. Vogelstein, Dr. Caffo, and Dr. Priebe, I extended our MRI connectomics pipeline for hyperparallelizability on AWS architecture and developed theoretical methods/software packages for manifold embedding techniques, independence testing, and graph statistics.

Vice President of Engineering
Jan 2018 - present

As the Vice President of Engineering, I am responsible for contributing technological vision, coauthoring technical documents and patents, and managing the software and data analytics teams. I also manage the delivery of internal technical projects.


  1. Joshua T. Vogelstein, Minh Tang, Eric W. Bridgeford, Da Zheng, Randal Burns, Mauro Maggioni. Linear Optimal Low-Rank Projection for High-Dimensional, Multi-Class Data. Submitted to Science.
  2. Cencheng Shen, Qing Wang, Eric W. Bridgeford, Carey E. Priebe, Mauro Maggioni, Joshua T. Vogelstein. Discovering Relationships and their Structures Across Disparate Data Modalities. Submitted to PNAS.
  3. GregoryKiar, Eric W. Bridgeford, WillGray Roncal, Consortium Reliability Reproducibility (CoRR), Vikram Chandrashakar, Disa Mhembere, Sephira Ryman, Xi-Nian Zuo,Daniel S Marguiles, RCameron Craddock, Carey E Priebe, Rex Jung, Vince D Calhoun, Brian Caffo, Randal Burns, Michael P Milham, Joshua T Vogelstein. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. Submitted to Nature Methods.
  4. Jordan Yoder, Li Chen, Henry Pao, Eric W. Bridgeford, Keith Levin, Donniell Fishkind, Carey E Priebe, Vince Lyzinski. Vertex nomination: The canonical sampling and the extended spectral nomination schemes. Submitted to Journal of Computational and Applied Statistics.
  1. Sarah F. Muldoon, Eric W. Bridgeford, and Danielle S. Bassett. Small-World Propensity in Weighted, Real-World Networks. Published in Nature, Scientific Reports (February 2016).
Works in Progress
  1. Carey E. Priebe, Youngser Park, Minh Tang, Avanti Athreya, Vince Lyzinski, John Conroy, Eric W. Bridgeford, and Joshua T. Vogelstein. On a 'Two Truths' Phenomenon in Spectral Graph Clustering.
  2. Shangsi Wang, Yang Zhi, Xi-Nian Zuo, Michael Miller, Cameron Craddock, Gregory Kiar, Will Gray Roncal, Eric W. Bridgeford, Carey E. Priebe, Joshua T. Vogelstein. Optimal Decisions for Discovery Science via Maximizing Discriminability: Applications in Neuroimaging. In Preparation.
  3. Eric W. Bridgeford, Heather Chappell, Martin Lindquist, Brian Caffo, Carey E. Priebe, Joshua T. Vogelstein. What is Connectome Coding? In Preparation.
These authors contributed equally to the work.
R Packages
  1. Eric W. Bridgeford, Censheng Shen, Shangsi Wang, and Joshua T. Vogelstein. Multiscale Graph Correlation. CRAN Package. DOI: 10.5281/zenodo.1246966.
  2. Eric W. Bridgeford, Minh Tang, Jason Yim, and Joshua T. Vogelstein. Linear Optimal Low-Rank Projection (LOL). CRAN Package. DOI: 10.5281/zenodo.1246978.
  3. Eric W. Bridgeford, Ronak Mheta, Coleman Zhang, and Joshua Vogelstein. Graph Statistics. Devtools-installable Package.
  4. Eric W. Bridgeford and Joshua T. Vogelstein. Statistical Learning Benchmarks. Devtools-installable Package.
Python Packages
  1. Gregory Kiar, William Gray Roncal, Disa Mhembere, Eric W. Bridgeford, Randal Burns, and Joshua T. Vogelstein. Neurodata MRI Graphs (ndmg). DOI: 10.5281/zenodo.595684.
  1. Gregory Kiar, William R Gray Roncal, Disa Mhembere, Eric W. Bridgeford, Shan gsi Wang, Carey Priebe, Randal Burns, and Joshua T Vogelstein. MR Graphs with Rich attribUTEs DataBase (Mr. GruteDB).. Organization of Human Brain Mapping (June 2016).
  2. Eric W. Bridgeford, Gregory Kiar, Will Gray Roncal, Disa Mehembre, Randal Burns, Joshua T Vogelstein. MRImages to Graphs: A One Click Community Pipeline for MR Connectome Analysis. Institute for Computational Medicine Night (March 2016).
  3. Gregory Kiar, et al. Community Connectomics via Cloud Computing Utilizing m2g - a Reference Pipeline. Organization for Human Brain Mapping (June 2015).
  4. Sarah F. Muldoon, Eric W. Bridgeford, Danielle S. Bassett. Quantifying Small Worldness in Weighted Brain Networks: Small-World Propensity. Society for Neuroscience (October 2015).
  5. Joshua T. Vogelstein, et al. The Open Connectome Project & Neurodata: Enabling Data Driven Neuroscience at Scale. Society for Neuroscience (October 2015).
  1. Joshua T. Vogelstein and Eric W. Bridgeford. Quantifying Differences between Diffusion and Functional Connectomes.. Society for Neuroscience (October 2017).
  2. Eric W. Bridgeford. From the Functional Brain to the Connectome: An Introduction to Neuroscience Research in the 21st Century. JHU Splash (2016).


In my free time, I enjoy playing guitar with friends, cooking, hiking, rock climbing, mountain biking, and building scale model warships.