R. Jerome Dixon

About

I’m a PhD student in Integrative Life Sciences at Virginia Commonwealth University, a Senior Operations Research Analyst at CANA LLC, and a Data Engineer/Data Scientist at SurgicalEd VR. I build decision-ready analytics and production ML systems where the stakes are clinical or operational — not experimental.

My work sits at the intersection of personalized medicine and supply-chain analytics, two domains where the wrong prediction has real consequences and where rigorous, explainable models aren’t optional.

Background

I hold an M.S. in Decision Analytics from Virginia Commonwealth University (2018), an M.S. in Information Technology Management from the Naval Postgraduate School (2007), and a B.S. in General Engineering from the U.S. Naval Academy (1997).

My professional experience spans operations research, data science, healthcare technology, and military logistics. Since 2017, I have served as a Senior Operations Research Analyst at CANA LLC. I have also worked as a Data Scientist at SurgicalEd VR since 2023 and served as a part-time Data Scientist with the University of Virginia School of Data Science from 2022 to 2026.

Before transitioning to civilian analytics roles, I served as a U.S. Navy Supply Corps officer from 1998 to 2016.

Research Interests

Medication-safety prediction:

modeling who is at near-term ED risk, and why

• opioid-related and polypharmacy-related emergency department events
• pharmacogenomic (PGx) signal recovery from administrative claims
• Virginia All-Payer Claims Database (APCD) cohorts
• gradient-boosted trees, temporal holdout, leakage control

Explainable attribution:

confirming which features and combinations drive risk

• SHAP importance and Formal Feature Attribution (FFA)
• Consensus-Risk labeling (SHAP ∩ FFA)
• drug–drug and multi-drug interaction structure
• FP-Growth networks and process-mining trajectories

Decision support:

shipping models without institutional EHR lock-in

• privacy-first serverless clinical dashboards
• medication-review prioritization prototypes
• partition-first claims engineering on AWS

Systems modeling:

structure, feedback, and connected data

• system dynamics
• graph databases and knowledge graphs

Application areas:

Pharmacogenomics, geriatric medication safety, pediatric heart transplantation, surgical simulation, supply-chain risk, system dynamics, graph databases