I didn’t grow up dreaming of being a scientist. Honestly, I didn’t even know what one was. My parents ran small sign businesses, and by the time I was a teenager, I was helping out—assembling vinyl and electric signs, crawling through attics to wire installations, handling invoices and paperwork. My world was practical, hands-on, and local.

At sixteen, I dual-enrollmed in a psychology course at my local college. One assignment asked us to research a mental illness using peer-reviewed sources. That was the first time I encountered a journal article. It felt like discovering a hidden world—one where people spent their lives trying to understand the human mind. I was captivated. I didn’t just want to read those articles—I wanted to write them. At the time, I was especially drawn to the question of why people engage in antisocial or harmful behavior.

I finished my AA at Indian River State College and applied to Florida State University with no real plan—just a vague understanding that if I wanted to get a PhD, I needed to get research experience.

I joined my first lab in my second semester, spending nearly 20 hours a week running sessions for online internalizing treatments in Dr. Jesse Cougle’s lab. Drawn to criminal behavior, I later joined Dr. Christopher Patrick’s lab, running EEG sessions focused on psychopathy and externalizing disorders.

But the more I learned, the more I realized how much we don’t know. My curiosity shifted—from what causes behavior, to how we even define and measure these elusive psychological constructs. I became fascinated with measurement itself: how latent traits like depression or disinhibition manifest across self-report, brain responses, and task performance—and how those tools often disagree.

During my post-bacc years, I trained under Dr. Keanan Joyner in advanced quantitative methods, working on scale development and multi-modal assessment. I became especially interested in how time shapes what we observe—how repeated measures across days, trials, or contexts offer different insights into psychological phenomena.

Today, my work revolves around three core aims:

1. Capturing dynamics at the trial level. I focus on breaking down averaged signals in cognitive tasks to examine trial-level fluctuations in performance and brain activity. I'm especially interested in the stability of a construct across time and trials, and have developed a method for estimating trial response consistency (tRC): a within-subject measure of variability that quantifies how reliable a metric is for an individual, on any given trial.

2. Understanding systematic measurement error. I’m investigating why daily symptom reports often diverge so dramatically from retrospective self-reports. How much of that gap is noise, and how much is telling us something meaningful about the limitations of our measurement tools?

3. Improving recruitment and data quality. I also study how we recruit and engage research participants. What draws people from different backgrounds to participate in our studies? What keeps them engaged? And what can we do to make participation feel more like collaboration than extraction?