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Student Spotlight: Audrey Byrne, MS’27

From Curiosity to Clinical Insight:
How Data Science Shapes Audrey Byrne’s Path to Surgery

For as long as she can remember, Audrey Byrne has wanted to become a surgeon. What began as a childhood fascination with mathematics and technology eventually evolved into a deeper understanding of how data shapes modern medicine. Through her experiences as both a clinical and basic science researcher, she saw how much information the healthcare system generates every moment—from vital signs to radiographic imaging to intra-operative metrics—and how often that data goes under-utilized. “As a pre-medical student, we aren’t taught how to unlock the value of that information,” she says. Pursuing data science felt like the missing piece in her academic trajectory, offering the tools to provide more individualized, evidence-based patient care.

Audrey was drawn to Dartmouth’s Master of Science in Health Data Science program because of its emphasis on bridging clinical medicine with advanced analytics. She also appreciated the small cohort structure, which fosters close mentorship and allows students to shape their learning around the questions that matter most to them. From working with real clinical datasets to collaborating directly with faculty on applied projects, Dartmouth offered an environment where Audrey could grow as a scientist, a thinker, and a future clinician.

Her education has already begun to transform how she thinks about medicine. Audrey now frames clinical questions not only in terms of physiology or surgical technique, but also through the lens of model structure, bias, uncertainty, and data availability. Learning R, Python, machine learning, biostatistics, and high-performance computing workflows—central elements of the HDS curriculum —has empowered her to analyze questions with newfound rigor. These skills have strengthened her desire to pursue academic surgery, where she hopes to integrate clinical practice with research in AI, predictive modeling, and surgical outcomes.

One of the most memorable aspects of her Dartmouth experience so far has been the sense of community. Audrey expected a more individualistic, technical environment. Instead, she found a cohort and faculty who are genuinely invested in one another’s success. This supportive culture helped her move from feeling like a beginner to independently debugging code, running advanced analyses, and interpreting complex models. The emphasis on understanding rather than memorization, she says, made a tremendous difference.

Looking ahead, Audrey envisions a career in which data science is seamlessly integrated into her clinical work. She hopes to continue developing predictive models for surgical recovery, designing AI-assisted decision tools, and contributing to large-scale outcomes research. Ultimately, she aims to be part of interdisciplinary teams that bring validated algorithms into real clinical settings, improving care for patients on a broad scale. Her interests closely align with opportunities in Dartmouth’s Health Data Science capstone experience , where students translate what they’ve learned into real-world impact.

For students considering or entering the program, Audrey offers this advice: be comfortable being a beginner! Stay open-minded. Data science can feel overwhelming at first, with new syntax, new ways of thinking, and seemingly endless learning curves. But asking questions, leaning on faculty and classmates, and maintaining a sense of curiosity make all the difference. “If you stay curious and persistent,” she says, “everything else falls into place.”

Written by: Mia Soucy

POSTED 11/25/2025 AT 10:28 AM IN #HealthDataScience #MS