Eric Bridgeford

Statistician & Project Management Expert

Eric Bridgeford

Statistician & Project Manager

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
Graph Inference
Statistical Theory
Dimensionality Reduction
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
January 2018 - September 2019

As the Vice President of Engineering, I was responsible for contributing technological vision, coauthoring technical documents and patents, and managing the software and data analytics teams. I also managed the delivery of internal technical projects. The company has since re-focused as Trimwire by Atana, LLC.


  1. Zeyi Wang, Eric W. Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. Statistical Analysis of Data Repeatability Measures.
  2. Gregory Kiar, 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.
  1. Eric W. Bridgeford, Shangsi Wang, Zhi Yang, Zeyi Wang, Ting Xu, Cameron Craddock, Jayanta Dey, Gregory Kiar, William Gray-Roncal, Carlo Colantuoni, Christopher Douville, Stephanie Noble, Carey E. Priebe, Brian Caffo, Michael Milham, Xi-Nian Zuo, Consortium for Reliability and Reproducibility, and Joshua T. Vogelstein. Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics. In production at PLOS Computational Biology (2021).
  2. Roger Peng, Athena Chen, Eric W. Bridgeford, Jeff Leek, and Stephanie Hicks. Diagnosing Data Analytic Problems in the Classroom. In production in the Journal of Statistics and Data Science Education (2021).
  3. Jaewon Chung, Eric W. Bridgeford, Jesus Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua T. Vogelstein. Statistical Connectomics. Published in the Annual Review of Statistics and Its Application (Volume 8, 2021).
  4. Ross M. Lawrence, Eric W. Bridgeford, Patrick E. Myers, Ganesh C. Arvapalli, Sandhya C. Ramachandran, Derek A. Pisner, Paige F. Frank, Allison D. Lemmer, Aki Nikolaidis, and Joshua T. Vogelstein. Standardizing human brain parcellations. Published in Nature, Scientific Data (March 2021).
  5. Joshua T. Vogelstein, Eric W. Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, and Mauro Maggioni. Supervised dimensionality reduction for big data. Published in Nature Communications (May, 2021).
  6. 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. Published in Journal of Computational Statistics and Data Analysis (May, 2020).
  7. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan Varjavand, Hayden Helm, and Joshua T. Vogelstein. GraSPy: Graph Statistics in Python. Published in Journal of Machine Learning Research (September 2019).
  8. Joshua T. Vogelstein, Eric W. Bridgeford, Benjamin D. Pedigo, Jaewon Chung, Keith Levin, Brett Mensch, and Carey E. Priebe. Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories. Current Opinions in Neurobiology (April 2019).
  9. Cencheng Shen, Eric W. Bridgeford, Qing Wang, Carey E. Priebe, Mauro Maggioni, and Joshua T. Vogelstein. Discovering Relationships and their Structures Across Disparate Data Modalities. Published in Elife (February 2019).
  10. Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, and Eric W. Bridgeford. On a ’Two Truths’ Phenomenon in Spectral Graph Clustering. In press at Proceedings of the National Academy of Sciences (PNAS).
  11. Joshua T. Vogelstein, Eric Perlman, Benjamin Falk, Alex Baden, William Gray Roncal, Vikram Chandrashekhar, Forrest Collman, Sharmishtaa Seshamani, Jesse L. Patsolic, Kunal Lillaney, Michael Kazhdan, Robert Hider, Derek Pryor, Jordan Matelsky, Timothy Gion, Priya Manavalan, Brock Wester, Mark Chevillet, Eric T. Trautman, Khaled Khairy, Eric W. Bridgeford, Dean M. Kleissas, Daniel J. Tward, Ailey K. Crow, Brian Hsueh, Matthew A. Wright, Michael I. Miller, Stephen J. Smith, R. Jacob Vogelstein, Karl Deisseroth, and Randal Burns. A community-developed open-source computational ecosystem for big neuro data. Published in Nature Methods (Nov. 2018)
  12. 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. Eric W. Bridgeford, Michael Powell, Gregory Kiar, Ross Lawrence, Brian Caffo, Michael Milham, and Joshua T. Vogelstein. Batch Effects are Causal Effects: Applications in Human Connectomics.
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. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan Varjavand, Hayden Helm, Joshua T. Vogelstein. GraSPy: Graph Statistics in Python. Pypi.
  2. 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.