David Ellis - Research Data Analyst

David Ellis

Research Data Analyst

Omaha, Nebraska, United States

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Summary

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Rockstar

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Top School

David Ellis is a research data analyst with 11 years of experience specializing in multi-modal medical image processing, visualization, and large-scale MRI workflow automation. Based in Omaha, he blends academic rigor—pursuing a PhD in Biomedical Informatics—with practical impact from industry roles managing multimillion-dollar imaging projects and improving CT analysis turnaround by 35%. He is an active open-source contributor in neuroimaging, having extended Nipype with FreeSurfer wrappers and optimized OpenMP support, and built a PyTorch 3D U-Net for medical image segmentation that integrates MONAI and data-augmentation controls. Comfortable bridging HPC-enabled research workflows and clinical operations, he brings both hands-on ML engineering and project leadership to translational imaging problems. An often-overlooked strength is his track record of turning complex imaging pipelines into reproducible, production-ready tools used across research and clinical settings.

11 years of coding experience

7 years of employment as a software developer

Doctor of Philosophy - PhD, Biomedical Informatics, Doctor of Philosophy - PhD, Biomedical Informatics at University of Nebraska Medical Center

Associates of Arts, Liberal Arts, Associates of Arts, Liberal Arts at Des Moines Area Community College

Master of Science (M.S.), Biomedical Engineering, Master of Science (M.S.), Biomedical Engineering at University of Iowa

English, Spanish

Github Skills (13)

mask-rcnn10

faster-rcnn10

pytorch10

machine-learning10

workflow-engine10

data-augmentation10

surf10

python10

medical-image-segmentation10

neuroimaging10

surface10

jupyter-notebook9

command-line-interface9

Programming languages (5)

C++ShellJupyter NotebookMATLABPython

Github contributions (5)

ellisdg/3DUnetCNN

Feb 2017 - Oct 2022

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

Role in this project:

ML Engineer / Data Scientist

Contributions:1 release, 848 commits, 94 PRs in 5 years 9 months

Contributions summary:David primarily focused on developing a 3D U-Net convolutional neural network (CNN) for medical image segmentation. Their contributions included adding a Jupyter Notebook for model training and inference, removing background iterations during training, and implementing a new model structure. The changes include updates for data augmentation by adding features for the user to specify data augmentation parameters. They also integrated the Monai package and updated the model architecture.

pytorchresnetmedical-imagedeep-learningimage-segmentation

nipy/nipype

Dec 2015 - Apr 2017

Workflows and interfaces for neuroimaging packages

Role in this project:

Back-end Developer

Contributions:202 commits, 10 PRs, 15 comments in 1 year 4 months

Contributions summary:David contributed to the development of FreeSurfer (FS) interfaces within the neuroimaging package, focusing on creating and improving wrappers for various FS commands. Their commits primarily involved adding wrappers for several FS command-line tools, like `mri_watershed`, `mri_ca_register`, `mri_normalize` and `mris_spherical_average`, allowing for the use of these tools within the larger Nipype workflow. The contributions also include integrating support for OpenMP with several FS tools to improve processing performance.

workflowneuroimagingpythondata-scienceworkflow-engine

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