Jill Kato for UC Irvine School of Social Sciences
Growing up in the rural Philippines, Kathleen Medriano learned early on that opportunity wasn’t always nearby. It could lie across towns, between disciplines, and even across oceans. At eight, she moved with her family to a farm so her parents could focus on their business. At eleven, she left home with her siblings to pursue a better education in a town two hours away. For college, she moved to Metro Manila. Through it all, she developed the ability to adapt, to work independently, and to see education not as a given but as something to be pursued.
Now a cognitive sciences Ph.D. student at UC Irvine in her fourth year, she’s interested in how people make decisions and how cognitive processes can be better captured through data-driven modeling.
Shifting course
In college in the Philippines, Medriano majored in psychology with plans to enter medicine. She imagined she might one day become a neurologist or neurosurgeon. While the courses she took opened her eyes to new ways of thinking, she found herself wanting a more structured way of understanding the mind. So, after graduation, she enrolled in a graduate program in mathematics, where she found a new kind of precision and a way of thinking that would later shape her research.
“Mathematics insists on abstract thinking. It teaches you to find shared structures across problems. That changed how I approached research,” she says.
After earning the graduate diploma, she took a job as a data analyst at a multinational financial technology company. During the pandemic, when she was forced to work from home, the distance between her job and her intellectual interests became harder to ignore.
“I wanted to return to the kinds of questions that had always interested me, like how people make decisions and what cognitive processes guide them,” she says.
At the time, she didn’t know that research, and eventually a Ph.D., would be the next step. Searching online, she came across a profile of a student from her university who had earned a master’s degree abroad and realized that maybe that was something she could do too.
She reached out to the student, and through that connection discovered a broader network of Filipino graduate students and postdocs, many of whom were mentoring others back home.
A meeting of minds
With support from that network, Medriano applied to programs worldwide. She focused on programs that would allow her to bring together her interests in the mind, mathematics, and data.
What set UC Irvine apart during the application process wasn’t just the strength of its cognitive sciences program. It was a 15-minute recruitment call that stretched into an hour and a half.
Medriano had originally applied to work with a different professor, but as part of the recruitment process was also scheduled to speak with cognitive sciences professor Joachim Vandekerckhove. What followed was less an interview than an idea-driven conversation.
“We were just brainstorming,” she says. “It turned into this open exchange of ideas. I realized pretty quickly that I could work with him.”
Vandekerckhove saw the same potential.
“Kath’s work sits at the intersection of cognitive science, statistics, and AI/ML,” he says, referring to artificial intelligence and machine learning. “This is an extremely fast-changing area of science and knowing that there are talented junior scholars tackling these subtle but important problems is both exciting and comforting.”
Early on, their conversations focused not only on technical fit, but on understanding where the field was heading.
“Outside of a research department it’s difficult to know what topics are interesting to the field now and in the near future. This is something we worked on together: to get a bird’s-eye view of the world of data-driven cognitive science.”
That shared way of thinking—analytical, interdisciplinary, forward-looking—has shaped their mentorship ever since.
Model discovery
Medriano is developing new approaches to cognitive modeling that challenge conventional assumptions.
“In research, it’s typical to choose a model and then fit it to your data. But that means your conclusions depend entirely on your chosen model—its structure and all the assumptions baked into it,” she says.
Medriano’s research inverts this procedure to discover plausible models that generated the data. Rather than beginning with a model, she starts with the data itself, using statistical techniques to let patterns and potential models emerge directly from it. She’s particularly interested in extending her methods to other areas in cognitive science where empirical findings often accumulate without a clear theoretical thread.
“A lot of findings get reported, but we don’t always know how they fit together. I think these methods can help bring more coherence,” she says about the applicability of her research.
In the coming years, she’s looking forward to collaborating across disciplines, especially with neuroscientists whose data may benefit from more flexible modeling frameworks.
“I hope to develop broader networks. The tools I’m working on are field-agnostic. They can apply to brain signals, behavior, even economic data. That kind of versatility excites me,” she says.
Making sense
Early in the program, Medriano also decided to pursue a master’s in statistics alongside her Ph.D., a path available to students with strong quantitative interests. Balancing both wasn’t easy. The dual program demands more coursework and technical depth than most students are asked to complete. But for Medriano, the combination has been foundational. And often, the most meaningful parts of her experience have come not from the research itself, but from the relationships it has made possible.
“There were two of us doing the stats masters. We struggled together. And shared struggles create deeper bonds,” she says.
Her community now includes close friends in her cohort, a tight-knit lab group, a local badminton circle, and a small but supportive Filipino academic network, two members of which are UC Irvine faculty.
Looking ahead, she imagines a future in research. What matters most is that the work stays intellectually alive and grounded in curiosity.
“I want to understand how we think in a way that doesn’t skip steps. One way to check that is by focusing on the data and seeing what potential models might have generated it,” she says. “There’s something about that inversion procedure that makes sense.”