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Work Experience
Work Experience
Current Role: Senior Research Engineer
Center for Biomedical Image Computing and Analytics
Center for Biomedical Image Computing and Analytics
Perelman School of Medicine, University of Pennsylvania
This role involves development of software applications which implement algorithms and techniques developed as part of research in the Center.
Lead developer of NeuroImagingChart (NiChart)
An ambitious project aiming to provide a "growth chart for the brain" for neurodegenerative disease via imaging biomarkers from structural, functional and diffusion MRI
Hybrid cloud-local application architecture leveraging cloud parallelism
Lead developer of the Cancer Phenomics Toolkit (CaPTk)
A C++ GUI workbench application for cancer analytics, including classical image processing algorithms as well as ML tools for prognosis and diagnosis
Lead developer of the Generally Nuanced Deep Learning Framework (GaNDLF)
A Python based framework designed to facilitate training of AI models for medical imaging tasks, with specialized handling of data augmentation and experiment specification
Center expert on "DevOps", containerization, CI/CD, cloud engineering
January 2020 - Present
Education
Education
Master of Science in Engineering (Scientific Computing) : 2023 - 2026
University of Pennsylvania, Philadelphia, Pennsylvania
Bachelor of Science (Bioengineering, minor Chemistry) : 2016 - 2018
Temple University, Philadelphia, Pennsylvania
Associate of Science (Mathematics and Natural Science) : 2014 - 2016
Delaware County Community College, Media, Pennsylvania
Publications
Publications
Coupled Cross-Sectional and Longitudinal Non-Negative Matrix Factorization Reveals Dominant Brain Aging Trajectories in 48,949 Individuals
In review
Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study
Neuro-Oncology, 2024
Best practices to evaluate the impact of biomedical research software—metric collection beyond citations
Bioinformatics, 2024
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
Nature Communications Engineering, 2023
Affiliations
Affiliations
MLCommons - Medical Working Group
MLCommons is an open collaborative effort to improve machine learning for everyone's benefit, with over 50 industrial members. From 2022 to 2023 I was a technical lead in the medical working group, which focuses on applications for medical data.
Talks and Presentations
Talks and Presentations
Federated Learning for Clinically Relevant AI
Educational Exhibit, Radiological Society of North America 2022
The Cancer Phenomics Toolkit: Human-in-the-Loop Machine Learning Segmentation
Lightning Talk and Poster, Information Technology for Cancer Research 2022
Competitions & Awards
Competitions & Awards
3rd Place of 27, WiCyS-Critical Infrastructure + Parsons CTF
"Capture the Flag"-style cybersecurity skills event hosted August 25, 2022.
My first event of this kind.
John and Lynne Ewell Engineering Scholarship
I was selected for this award in Spring 2018 from among all Temple University College of Engineering students based on merit and need.
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