Thea L. Enache

I grew up between two places: Romania, my first language and where my familial lineage is rooted, and the Bay Area, where I spent the majority of my adolesence. Both of those places shaped how I see the world and what I care about. In Romania, I saw up close how inequity shapes health outcomes in a system that is underfunded, understaffed, and often corrupt. In the Bay Area, I saw up close how innovation can be a powerful force for good but also how it can exacerbate inequities when it's not built with everyone in mind.

In Romania, care was something you and your family negotiated constantly. What happened to you in a medical setting depended almost entirely on who you slipped a gift to at the door, at the desk, at the cardiologist, wherever you ended up. Records were handwritten on paper, and symptoms were only addressed if you were lucky and had paid enough people by the time you needed it. I saw people I loved operated on with tools that should not have been used. Consequentially, I understood very early on that medicine was not neutral, that systems were not neutral, but rather that who you are and what you have determines what you receive, often long before you're sick.

In California, the Bay Area taught me to believe in what technology could do. I grew up taking things apart with my father, convinced that innovation could solve things. In fourth grade I was scared to take my hands off the handlebars to signal a turn, so I built lights that would do it for me: water bottle tops, a plastic box, LEDs, spare parts. I loved building things that solved something. But slowly and then all at once, the Bay Area also showed me who the big innovations were actually built for. A different flavor of a similar problem.

I'm somewhere between those two places, academically and culturally. That used to feel like a disadvantage, but I've since found my footing in it.

I'm Thea Enache, completing dual degrees in Public Health and Mathematical Data Science + Computer Science at San Diego State University. My work is at the intersection of public health, AI, and computational modeling. Sometimes that's epidemiological modeling, sometimes it's building clinical AI tools, sometimes it's just asking whether what we've already built is actually reaching the people it should. I want to work on that problem across global health epi, health-focused AI, and the policy questions that determine whether any of it matters on the ground.

Please feel free to pick around this page to follow along on my journey :)