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.
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.
0%: Beginner, 50%: Proficient, 100%: Expert
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.
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.
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.
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 manage the delivery of internal technical projects.
In my free time, I enjoy playing guitar with friends, cooking, hiking, rock climbing, mountain biking.