🏥I'm Mark Aronson (he/him), a bioengineer turned computational modeler with a passion for tightening the feedback loop between policy design and implementation by making government data more accessible, contextualized, and actionable.
🔗 I currently work as a Data Activation Engineer within the General Service Administration's Technology Transformation Services Department, where I am a member of an agentic infrastructure consulting team housed in the Engineering Division and the Centers of Excellence.
🏛️ I first came into government 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 Agentic Infrastructure Consulting team, I operate across a few areas:
🧪 Building MCP servers for public data at the NIH, including servers for NIH RePORTER, NCBI Eutils, and ClinicalTrials.gov's APIs.
🎲 Everything that happens in biological systems is driven by random, stochastic biomolecular interactions. But from this randomness, we get robust development behaviors as a single cell divides into a clonal population. In my main paper from grad school, I investigated this paradox using compouational modeling and theory to derive a set of rules that describe what biochemical reaction properties lead to similarity between related cells (preprint and code).
➕ 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!