Olmo Zavala Romero is an assistant professor in the Department of Scientific Computing, part of Florida State University’s College of Arts and Sciences. Zavala Romero earned his bachelor’s degree in computer software engineering from the Monterrey Institute of Technology and Higher Education in 2004 and his first master’s degree in computer science from the National Institute of Astrophysics, Optics and Electronics in 2008, both in Mexico. At FSU, Zavala Romero earned two degrees in computational science — a second master’s degree in 2013 and a doctorate in 2015 — before becoming an assistant professor at the National Autonomous University of Mexico in 2016. He returned to FSU three years later as a research faculty member at the Center for Ocean and Atmospheric Prediction Studies, where he remains an affiliated faculty member.

What inspired you to choose your field of study?

Twenty-five years ago, scientific computing, which uses mathematical computer models to solve complex science and engineering problems, didn’t exist in its current form. My interest in computer science began in my youth when I enjoyed playing video games but didn't have a console, just old computers my dad didn’t use. My brother and I learned simple ways to run computer games more efficiently, and from that I became interested in computer science.

Can you break down your areas of research for us?

One of my main research areas is applied machine learning, a type of artificial intelligence that uses mathematical models and algorithms to help computers learn and make predictions from data. I apply ML specifically to oceanography and medical imaging. I also develop scientific visualization software, which creates visual representations of high-dimensional data in an interactive and efficient manner. Specific projects include using ML to automatically identify prostate cancer from MRI images or to assimilate physical observations into numerical models of oceanic phenomena.

What do you want the public to know about your research? Why are your topics important?

Newer and better data sources combined with improved computer algorithms and faster computers give us the opportunity to solve previously impossible problems. One of my research areas focuses on how to better interpret satellite data to improve ocean forecasts using machine learning. Accurate predictions of the ocean's state are important for predicting hurricane intensification, storm surges, or even the location of an oil slick in the case of oil spills. In medical imaging, my research can help create fast and affordable computer-based medical diagnoses, serving as a guide for specialists or as a final diagnosis in areas lacking easy access to experts.

What makes you passionate about your topics of research?

I like to work on projects that provide short-term benefits. I also enjoy being interdisciplinary, learning from other domains, and integrating the best techniques in ML and computational science with the latest available data to solve problems.

What is your favorite part of your job?

I love the freedom to do research in my area of interest and the opportunity to work on solving any problem I consider relevant. I also enjoy working with students — their energy, diverse perspectives, and seeing how behaviors change between generations.

How did your time at FSU prepare you for professional success?

My time as an FSU student gave me relevant tools in different areas. I was fortunate to have amazing professors in the Department of Scientific Computing, some who are now my colleagues. I gained theoretical and practical knowledge relevant to my field, built international networks by sharing my research at conferences, and made great friends from diverse backgrounds. This diversity reinforced my understanding that perspectives are based on one's history and background.

What is your best memory so far from working at FSU?

Some of my favorite memories include having lunch and beers with colleagues while discussing topics ranging from science, research, politics, kids and sports. I also value “eureka” moments of understanding with students.

Who are your role models? Are there certain people who have influenced you most in your life and career?

When I was younger, my role models were athletes like Michael Jordan and Jan-Ove Waldner, commonly called the "Mozart of table tennis." Now, I believe everyone has something to teach me. For people who have influenced me the most, I have to say my parents; they are honest, hardworking scientists who are always helping others. Other influences include FSU professor emeritus Janet Peterson who started my interest in scientific computing and Gordon Erlebacher – program director of FSU’s Interdisciplinary Data Science Master’s Degree Program and professor of scientific computing – who has broad knowledge in different areas and a sense of curiosity and excitement that are enviable even for a kid. Another influence is my doctoral advisor, professor of scientific computing Anke Meyer-Baese, who always looks out for her students’ wellbeing.

Do you have any exciting upcoming projects or goals you’re working toward?

I'm enthusiastic about a software tool we’re developing called NcDashboard, which we hope will transform data exploration in earth sciences. It combines web visualizations and large language models to perform efficient and dynamic data analysis and visualization.

Another exciting project is led by one of the department’s doctoral students, Jose Miranda, who is exploring new ways to relate the ocean surface state with the deeper ocean, which is important due to the limited observations below the surface.

If your students only learned one thing from you (of course, hopefully, they learn much more than that), what would you hope it to be?

I tell almost all my groups that if I could summarize what I learned in undergrad in one phrase, it would be “la culpa la tengo yo,” meaning “it’s my fault.” This phrase started from the idea of taking responsibility when a computer program doesn't work. Instead of blaming everything else, like the computer, the compiler, or your teammates, start by considering what you might’ve done wrong. This approach helps you find solutions faster and can be applied to many aspects of life, including sports, cooking and social relationships.