From teaching three-year-olds in Spain to developing cutting-edge statistical models for clinical trials, Dr. Alejandro Villasante-Tezanos brings a unique perspective to biostatistics education and research at UTMB.
When Dr. Alejandro Villasante-Tezanos describes his journey to becoming a biostatistics professor, his eyes light up with the same curiosity he hopes to instill in his students. "I'm much more worried about teaching them the love for my subject," he explains from his office in the School of Public and Population Health. "If they have that, they're going to continue on."
This philosophy stems from a lifetime of learning that began in Murcia, Spain, where Villasante-Tezanos grew up in a family of teachers. He initially pursued engineering in high school but found himself drawn to mathematics, partly to impress a classmate. After finding his undergraduate program in pure mathematics too theoretical, he discovered statistics offered the perfect blend of mathematical rigor and real-world application he craved.
From Maryland Classrooms to UTMB Research Labs
Villasante-Tezanos's teaching career has taken him across continents and age groups. He began as an exchange teacher in Maryland, where he taught mathematics in both English and Spanish. When the financial crisis hit Spain particularly hard, he and his wife, whom he met in the United States, made the difficult decision to return to America.
"I've taught from three-year-olds to postgraduate students," Villasante-Tezanos says with a laugh, recalling his early days teaching in Spain. This diverse experience shapes his approach to education today. He emphasizes adaptation and meeting students where they are, recognizing that "every learner, every person, is different."
Solving Clinical Trial Recruitment Challenges Through Statistical Innovation
Currently serving as Assistant Professor in the Department of Biostatistics and Data Science, Villasante-Tezanos is making significant contributions to clinical trial methodology. His recent work, presented at the Society for Clinical Trials conference in Vancouver, addresses a critical challenge in medical research: predicting patient recruitment.
"In clinical trials, there are a lot of difficulties to reach the proposed number of subjects," he explains. This challenge has serious implications. If trials don't recruit enough participants, they may be underpowered, potentially leading to incorrect conclusions about treatment effectiveness. In life-threatening disease research, such errors could have devastating consequences.
Villasante-Tezanos and his colleague Dr. Xiaoying Yu have developed a non-parametric approach that helps researchers predict recruitment patterns early in a trial. Their method accounts for real-world factors like seasonal effects and holiday breaks that traditional models often miss. "Our method, in conjunction with good marketing strategy, is essential for the success of clinical trials," he notes.
The innovation has already gained attention in the field. Their work has been published in BMC Medical Research Methodology, and they've created an R package available through CRAN, making their method accessible to researchers worldwide. At the recent conference, Villasante-Tezanos was pleased to discover other research groups are now incorporating seasonality into their models, validating their approach's impact.
Balancing AI Integration with Essential Mathematical Foundations
As artificial intelligence transforms education and research, Villasante-Tezanos thoughtfully considers its role in his teaching. "I'm still trying to figure out how to embed AI into the teaching so that we can actually assess students," he reflects. His approach is pragmatic: while AI will likely be used in almost every job in the future, students still need foundational knowledge to add value and use these tools effectively.
"Foundations should be AI-free," he emphasizes. "Once you have those foundations, then you definitely have to add a skill set of using AI to complement what you do."
Making Statistics Accessible for Public Health Students
In his role as MPH biostatistics track program director and chair of the SPPH faculty assembly, Villasante-Tezanos is committed to making statistics accessible and even enjoyable for public health students. He regularly ends his classes by encouraging students to visit during office hours, working to break down the barriers that often prevent students from seeking help.
"Math is a hated subject for most people," he acknowledges, often due to negative past experiences. His goal is to help students move past these barriers by showing them how quantitative skills apply to real-world public health challenges.
Learning Beyond the Laboratory
Outside the classroom, Villasante-Tezanos embodies the curiosity he teaches. A self-described "renaissance type of learner," he tackles everything from plumbing to car repair, learning from what he calls "YouTube University." Currently, he's building a barbecue, changing his bedroom flooring, and replacing a head gasket on one of his cars.
This hands-on approach to learning mirrors his research philosophy. Just as he teaches students to love learning for its own sake, he continues to pursue knowledge across disciplines. "I do it because I love it," he says simply.
Fostering Curiosity Over Perfect Mastery
As Villasante-Tezanos continues his work at UTMB, he remains focused on breaking down barriers between students and statistics, and between theoretical models and real-world clinical trials. His message to students is clear: curiosity and love for learning matter more than perfect mastery of techniques.
"If they get that love from me," he says of his students, "maybe they may not master some of this stuff as well as I would hope, but they'll get it later on."
To learn more about Dr. Villasante-Tezanos's research on clinical trial recruitment prediction, read the full publication in BMC Medical Research Methodology.
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