Eric Bridgeford

Postdoctoral Scholar & Statistician
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Eric Bridgeford

Postdoctoral Scholar & Statistician

Hello! I'm Eric Bridgeford. I am a postdoctoral scholar at Stanford University working towards developing statistical methods for neuroscience research. I am interested in end-to-end decision making in biological datasets, summarized in part by the below figure:

In many fields such as neuroimaging with expansive datasets, translating insights into clinical practice has proven problematic at best. I believe that disparities that arise across data collection sites in the form of batch effects, difficulties tuning analyses to properly adjust for potential confounding biases, difficulties identifying best practices for the construction of data derivatives (data pre-processing), and difficulties understanding how insights will translate when other pre-processing steps are taken, may be a potential source of this problem.

demoEducation

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
Ph.D. in Biostatistics
September 2018 - August 2023

I completed my graduate education at Johns Hopkins University in the Department of Biostatistics. I was be supervised by Dr. Brian Caffo and Dr. Joshua Vogelstein. My work focused on statistical methods for understanding measurement error in connectomes.

demoSelected Skills

Statistical
Package Development
90%
Graph Inference
90%
Statistical Theory
80%
Dimensionality Reduction
75%
Timeseries
70%
Programming
R/python
90%
Object-Oriented Programming
75%
Scalable Computing
70%
Bash
60%
C/C++
60%
Leadership
Communication
90%
Product Delivery
80%
Feedback
80%
Flexibility
70%
SCRUM
70%

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

demoExperience

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
NeuroData
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 - Winter 2024

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.

atana
Vice President of Engineering
Atana
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 Operator.io by Atana, LLC.

Postdoctoral Scholar
Stanford University
Winter 2024 - present

With Dr. Russell Poldrack and Dr. Maya Mathur, I work on synthesizing and applying concepts from causal inference to problems in neuroimaging.

Lecturer
Johns Hopkins University
2023 - present

I provide a course on Introduction to Graph Models, as part of the Advanced Graph Analytics module through the Engineering for Professionals Program with Dr. Will Gray Roncal.

demoProjects

Submitted and Works in Progress
  1. Eric W. Bridgeford, Jaewon Chung, Robert J. Anderson, Ali Mahzarnia, Jacques A. Stout, Hae Sol Moon, Zay Yar Han, Joshua T. Vogelstein, Alexandra Badea. Network Biomarkers of Alzheimer's Disease Risk Derived from Joint Volume and Texture Covariance Patterns in Mouse Models. Under review at PLoS One (2025).
  2. Eric W. Bridgeford, Brian Caffo, Maya B. Mathur, Russell A. Poldrack. How causal perspectives can inform problems in computational neuroscience. Under review at Nature Neuroscience (2025).
  3. Jaewon Chung, Eric W. Bridgeford, Michael Powell, Derek Pisner, Ting Xu, Joshua T. Vogelstein. The Heritability of Human Connectomes: a Causal Modeling Analysis.
  4. Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Ashwin DeSilva, Censheng Shen, Brian Caffo, Joshua T. Vogelstein. Learning sources of variability from high-dimensional observational studies.
  5. Vivek Gopalakrishnan, Jaewon Chung, Eric W. Bridgeford, Benjamin D. Pedigo, Jess Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, Joshua T. Vogelstein. Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes.
  6. Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package.
  7. 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.
Peer-Reviewed Journal Articles
  1. Eric W. Bridgeford, Michael Powell, Gregory Kiar, Stephanie Noble, Jaewon Chung, Sambit Panda, Ross Lawrence, Ting Xu, Michael Milham, Brian Caffo, Joshua T. Vogelstein. When no answer is better than a wrong answer: a causal perspective on batch effects. Published in Imaging Neuroscience (2025).
  2. Zeyi Wang, Eric W. Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. Statistical Analysis of Data Repeatability Measures. Published in International Statistical Review (2024).
  3. Benjamin D. Pedigo, Mike Powell, Eric W. Bridgeford, Michael Winding, Carey E. Priebe, Joshua T. Vogelstein. Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome. Published in eLife (2023).
  4. Ting Xu, Gregory Kiar, Jae Wook Cho, Eric W. Bridgeford, Aki Nikolaidis, Joshua T. Vogelstein, Michael P. Milham. ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences. Published in Nature Methods (2023).
  5. 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. PLOS Computational Biology (2021).
  6. 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).
  7. 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).
  8. 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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. 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).
  14. 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).
  15. 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)
  16. 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).
Peer-Reviewed Conference Proceedings
  1. Qingyang Wang, Michael A. Powell, Ali Geisa, Eric W. Bridgeford, Carey E. Priebe, Joshua T. Vogelstein. Why do networks have inhibitory/negative connections? In Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV, 2023).
  2. Qingyang Wang, Michael Alan Powell, Eric W. Bridgeford, Ali Geisa, Joshua T. Vogelstein. Polarity Is All You Need to Learn and Transfer Faster. International Conference of Machine Learning (ICML, 2023).
† These authors contributed equally to the work.
Books
  1. Eric W. Bridgeford, Alexander Loftus, Joshua T. Vogelstein. Hands on Network Machine Learning. Publishing contract with Cambridge University Press (2024).
  2. Eric W. Bridgeford. Measure Theoretic Probability Theory. Work in progress.
Book Chapters
  1. Eric W. Bridgeford, Daniel Sussman, Vince Lyzinski, Yichen Qin, Youngser Park, Brian Caffo, Carey Priebe, Joshua T. Vogelstein. What Is Connectome Coding?, In: Functional, Structural, and Molecular Imaging, and Big Data Analysis (edited by E. Boyden and K.Chung), pp. 63 - 74, Society for Neuroscience (2018).
R Packages
  1. Eric W. Bridgeford, Michael Powell, Brian Caffo, Joshua T. Vogelstein. causalBatch: Causal Batch Effects. CRAN Package (2024).
  2. Eric W. Bridgeford, Censheng Shen, Shangsi Wang, and Joshua T. Vogelstein. Multiscale Graph Correlation. CRAN Package (2019). DOI: 10.5281/zenodo.1246966.
  3. Eric W. Bridgeford, Minh Tang, Jason Yim, and Joshua T. Vogelstein. Linear Optimal Low-Rank Projection (LOL). CRAN Package (2018). DOI: 10.5281/zenodo.1246978.
  4. Eric W. Bridgeford, Ronak Mheta, Coleman Zhang, and Joshua Vogelstein. Graph Statistics. Devtools-installable Package.
  5. Eric W. Bridgeford and Joshua T. Vogelstein. Statistical Learning Benchmarks. Devtools-installable Package.
Python Packages
  1. Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein. Hyppo. Pypi Package (2018).
  2. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan Varjavand, Hayden Helm, Joshua T. Vogelstein. GraSPy: Graph Statistics in Python. Pypi Package (2018).
  3. 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).
Courses
  1. Eric W. Bridgeford and Jaewon Chung. Hands on Network Machine Learning. Continuing Education Course at: Joint Statistical Meetings (JSM, 2023).
Invited Lectures
  1. Eric W. Bridgeford. Hands on Network Machine Learning. Course module for: Johns Hopkins University Applied Physics Laboratory (APL) Advanced Graph Analytics (2024).
  2. Eric W. Bridgeford and Jaewon Chung. Hands on Network Machine Learning. Course module for: Johns Hopkins University Applied Physics Laboratory (APL) Advanced Graph Analytics (2023).
  3. Eric W. Bridgeford. Introduction to Network-Valued Data. Guest Lecturer for: Introduction to Computational Medicine Course at Johns Hopkins University (2023).
  4. Eric W. Bridgeford. Community Detection. Guest Lecturer for: Introduction to Computational Medicine Course at Johns Hopkins University (2023).
  5. Eric W. Bridgeford. Introduction to Network-Valued Data. Guest Lecturer for: Introduction to Computational Medicine Course at Johns Hopkins University (2022).
  6. Eric W. Bridgeford. Community Detection. Guest Lecturer for: Introduction to Computational Medicine Course at Johns Hopkins University (2022).
  7. Eric W. Bridgeford. Unsupervised Machine Learning with Connectomics Data. Course module for: ABCD ReproNim Course (2022).
Miscellaneous Talks
  1. Eric W. Bridgeford. GrasPy: Causal Analyses on Populations of Graphs. PI meeting talk at: National Institutes of Mental Health (2023).
  2. Eric W. Bridgeford. Hands on Network Machine Learning. Lightning round talk at: Berlin Connectomics Meeting (2022).
  3. Eric W. Bridgeford. A Principled Approach to Statistical Connectomics and Mega-Analysis. Oral Session at: Organization of Human Brain Mapping (OHBM, 2018).
  4. Joshua T. Vogelstein and Eric W. Bridgeford. Quantifying Differences between Diffusion and Functional Connectomes. Society for Neuroscience (2017).
  5. Eric W. Bridgeford. From the Functional Brain to the Connectome: An Introduction to Neuroscience Research in the 21st Century. JHU Splash (2016).

demoInterest

In my free time, I enjoy playing guitar in 80s hair metal bands, building and tinkering with guitar-related equipment, hiking, and rock climbing.