🏥I'm Mark Aronson (he/him), a bioengineer turned computational modeler with a passion for advancing performance and equity in the U.S. healthcare system by improving healthcare policy through an iterative design process of theory- and data-driven modeling.

🏛️ Currently, I serve as a Data Scientist at the NIH's SCHARE program, housed within the National Institute on Minority Health and Health Disparities.

🥼 I previously completed my Ph.D. in the Sgro Lab at Boston University's Department of Biomedical Engineering and followed the lab for a short Postdoctoral Fellowship at the Howard Hughes Medical Institute's Janelia Research Campus.

As a member of the SCHARE team, I support the platform across a few areas:

🛠️ I support the development of the SCHARE Data Repository, where public bugs are reported and tracked here.

💻 I also develop tools to ease access to data for health research, including fedwrap, a Python package designed to make pulling data from federal datasets (American Communities Survey, CDC PLACES, and CDC BRFSS) easy as 🥧!

👨‍🏫 I help design curricula and training materials for the data science training seminar series run by the SCHARE team, which you can view on YouTube.

📊 I provide data science and machine learning expertise to researchers performing health outcomes research on the cloud platform Terra.

➕ As a trained engineer, back-of-the-envelope calculations are a critical tool for gaining a quantitative intuition for any system. These estimates can even go so far as to be a gut check for new phenoemena, which is exactly how I used them in this paper. By deriving simple, theory-based equations, I was able to provide a quantitative validation on a novel stress fiber phenomena discovered in the Sgro Lab. You can check out the underlying calculations in this repo.

🔁 Research reproducibility is one of the most compelling reasons for using scripted programming for data analysis in science. I streamlined the model code and reproduced all the results for an upcoming publication from the Sgro Lab which you can find in this repo.

🧫 How to use engineering design principles to understand and program biology is the main reason I went to graduate school, and an idea I became further entranced by the more I studied it. I led the writing of a current opinion piece on strategies of using molecular noise as an engineering design principle for multicellular systems and contributed to a review of synthetic multicellularity with members of the Sgro Lab.

📬 You can find me on LinkedIn here!