About

Most academic websites skip the personal. I think that is a mistake.

In Bayesian inference, you are required to declare your priors before examining data. Priors are not a flaw. They are part of how inference works. What matters is whether you name them. Researchers carry priors too. We bring assumptions about what questions matter, whose experiences generalize, what counts as evidence. The life we lived before the work shapes the questions we think to ask. Acknowledging that does not compromise the work. It is just part of doing it thoughtfully. The sections below are my attempt to be transparent about mine — the background, the experiences, and the tensions that brought me to the questions I study.

early context & home.

I grew up in rural San Diego County. Neither of my parents had a four-year degree. Most adults I knew worked in trades, small businesses, or service jobs. Academia was not something I inherited. It was something I found later and had to learn from the outside in.

Being first-generation in academic spaces means you spend a lot of time translating. I move between worlds that do not always recognize each other. That experience shapes how I write and who I write for. I try to make my work legible to people who are not already inside it.

formative political years.

My mother was liberal. My father was politically conservative but socially progressive in ways shaped by his Christian faith. They disagreed sharply on policy but shared a moral seriousness about fairness and responsibility. I spent a lot of my formative years in that tension.

Growing up that way made me pay attention to how people talked about politics, not just what they believed. What leads people who share so much — a household, a set of values, a moral framework — to attach to such different political coalitions? How does someone's faith, or class background, or the community they grew up in, shape the political conclusions that feel right to them? That curiosity stuck with me.

soccer career.

Before I entered academia, my entire life revolved around soccer. I was part of the early wave of the ACL epidemic that disproportionately affects women athletes. I tore my ACL three times before finishing high school. My first surgery, in eighth grade, was botched by a surgeon who did not take a young woman's athletic career seriously.

I came to understand that this was not simply an individual failure. The research, the protocols, the recovery standards had been built around male athletes. Women's sports medicine was an afterthought. That experience made gender inequality in institutions feel concrete to me before I had any theoretical language for it.

I rebuilt and earned a scholarship to play college soccer at Francis Marion University, a small state school in Florence, South Carolina. That is how a kid from San Diego ended up on the opposite coast. The school was small and pedagogy-focused. Small classes, professors who knew my name, and faculty who genuinely cared about every student's outcome. That environment shaped how I think about my own classroom.

what I study now.

I sit on the cusp between millennial and Gen Z. I watched the early internet become the platform environment we now inhabit. I watched friends and neighbors drift into media spaces that felt less like communities and more like pipelines. I saw how political content could travel through entertainment, sports, and lifestyle media without ever announcing itself as political.

That observation is what drew me to studying gendered media spaces. My current work asks how digital information environments sort people by gender before they encounter explicitly political content, and what that sorting does to political identity and vote choice. In the podcast ecosystem, masculine and feminine audience preferences lead to structurally different political exposure. Men and women consuming media for non-political reasons end up politically informed in very different ways.

I am also drawn to methodology because of what standard tools can hide. Conventional approaches to studying identity often flatten how race, gender, and class operate together. Part of my work is building better tools for capturing that complexity. I think rigor and nuance are compatible. Most of my papers are trying to demonstrate that.