DrPH Portfolio.

This was where the academic theory of "real-world data" became real. Working inside a payer environment taught me that the most rigorous methodology is only as useful as the audience that can act on it; propensity score matching mattered to my dissertation, while variable definitions and feasibility constraints mattered to the team. Translating between the two became the work.

The dissertation moved through many rounds of re-design, re-code, testing, and revision. Each iteration deepened my understanding of how cohort decisions, ICD coding choices, and proxy variables shape what claims data can actually answer. Along the way I learned to raise analytical concerns with my advisor and co-authors clearly enough that they could be acted on rather than simply noted, a communication skill I now consider as central to the work as the analytic skill itself. I also learned to treat thousands of lines of analytic code as living infrastructure: writing documentation alongside the work, building reusable functions, and developing a troubleshooting practice that has saved many times the time it took to build.

The experience cemented for me that real-world evidence is not a single method; it's a negotiation between rigor, data fitness-for-purpose, stakeholder use, and the patience to revise.

Holding a multi-stream research portfolio across telehealth, cancer, autism, and eating disorders forced me to think beyond a single methodology and become flexible across study designs. SAS for SEER-Medicare and Medicaid VRDC claims, R for the HINTS analysis, qualitative coding and PRISMA review for the secretive-eating systematic review and patient interviews. Each project demanded a different stance toward data and a different vocabulary for the audience.

The breadth itself taught me as much as the depth. Switching between national survey work (HINTS, NHIS, NSCH), linked registry-claims data (SEER-Medicare), and mixed-methods qualitative analysis built a working intuition for which data structures can answer which questions, and which questions are better reframed before they ever reach a model. Co-authoring across multiple research teams sharpened the skill of writing in a shared voice. That practice fed directly into my first-authored publication in Digital Health, my co-authored manuscripts on autism disparities and dementia caregiving, and my poster and invited presentations at APHA and the Penn State CTSI Collaborative Event.

The thread across all of it was the same question: who is and isn't getting care, and why? Being a research scientist taught me to keep that question stable while letting methods, datasets, and stakeholders adapt.

Leadership outside the lab was, for me, the most honest test of what I'd been writing in my Leadership Philosophy. Adaptive leadership reads cleanly on paper; co-leading a 10-person committee alongside two fellow co-chairs through industry panelist recruitment, brochure deadlines, and on-site logistics is something else, and serving as event coordinator and on-stage host is another layer again. I learned that "giving the work back" is a discipline, not a default, and that protecting team members' voices through disagreement is harder than facilitating consensus. Co-leading also required a kind of horizontal communication I had not previously practiced at scale: aligning decisions across three co-chairs without slowing the work down. Hosting and delivering a 2-day event for graduate and postdoctoral trainees reinforced that translation skills (turning vision into operations, turning operations into stories, turning a room of strangers into an engaged audience) are research skills at a different scale.

TA-ing PHS 507 (Public Health Surveillance) and PHS 535 (Quality of Care Measurement) was where my Teaching Philosophy started taking shape. I came in expecting to explain statistical programming; I left understanding that the harder craft is meeting a learner where they are without taking shortcuts that would shortchange them later. Office hours taught me to listen for the actual question underneath the question; the students who initially hesitated to ask anything were the ones for whom the deepest framing mattered most. Teaching also taught me that articulating a method out loud, in real time, sharpens my own analytic thinking, a lesson that continues to pay back in every research conversation.