Pathways Into Mathematical Oncology

What does a career in Mathematical Oncology look like?

We spoke with leading researchers from across the country who are combining mathematics, computer science, and biology to push the boundaries of cancer research. In these interviews, they share how they got started, what their day-to-day looks like, and the advice they’d give to students curious about this powerful and emerging field.

Whether you're into data, medicine, or math modeling — their stories show just how impactful a STEM path can be.

Interview with Dr. Thomas Yankeelov

In this interview, Dr. Thomas Yankeelov, Director of the Center for Computational Oncology at The University of Texas at Austin, shares his work at the intersection of biomedical engineering and medicine. With joint appointments in Biomedical Engineering and the Dell Medical School, Dr. Yankeelov focuses on using advanced imaging and mathematical modeling to predict tumor growth and treatment response, with the goal of personalizing and improving cancer care.

Interview with Dr. Naoru Koizumi

In this interview, Dr. Naoru Koizumi—a professor and associate dean at George Mason University and an expert in biomedical data analytics—discusses the power of mathematical modeling in addressing complex healthcare challenges. She shares her insights on interdisciplinary collaboration, the growing role of AI in healthcare, and the joy of mentoring high school students in biomedical research.

Interview with Professor Yang Kuang

In this interview, Dr. Yang Kuang, a professor at Arizona State University and author of Introduction to Mathematical Oncology, explores the critical role of mathematical models in advancing cancer research. Dr. Kuang discusses how mathematical frameworks are used to predict tumor growth, simulate treatment outcomes, and enhance our understanding of cancer biology.

Interview with Dr. Russell Rockne

In this interview, Dr. Russell Rockne, Director of Mathematical Oncology at City of Hope, talks about how mathematical models and machine learning are transforming cancer research and treatment. He explains how these tools help predict tumor growth and personalize therapies, offering a glimpse into the future of personalized medicine.

Interview with Dr. Stacey Finley

In this interview, Dr. Stacey Finley, a professor at the University of Southern California with a background in chemical engineering, shares her journey into the field of mathematical oncology. Dr. Finley discusses how computational models can predict treatment outcomes, explore tumor-stroma interactions, and help tailor therapies like CAR T cells to individual patients. She highlights the power of interdisciplinary collaboration and the growing role of data-driven approaches in advancing personalized cancer treatment.

Interview with Dr. Sara Hamis

In this interview, Dr. Sara Hamis, Assistant Professor in Machine Learning at Uppsala University in Sweden and leader of the AIMOn research group, discusses her award-winning research on how cancer cells respond to DNA-damaging drugs. Combining AI, mathematics, and simulations, her work focuses on treatment resistance and tumor development. She also shares her international experience and commitment to making these tools accessible to researchers and students worldwide.

Interview with Dr. Jana Gevertz

Dr. Jana Gevertz, a mathematics professor at The College of New Jersey and expert in mathematical oncology, discusses how math is used to improve cancer treatment by modeling drug resistance, tumor behavior, and treatment outcomes. She explains concepts like pre-existing vs. drug-induced resistance, phenotypic plasticity, and virtual clinical trials, while offering practical advice for students interested in using math, biology, and computer science to solve real-world medical challenges.

Interview with Dr. Jason George

Dr. Jason T. George, an Assistant Professor at Texas A&M University specializing in biomedical engineering, discusses how mathematical models and machine learning are transforming our understanding of cancer biology, immune interactions, and treatment resistance. He explores how stochastic models can capture the unpredictable nature of tumor behavior, while also diving into the use of cutting-edge tools like AlphaFold 3 and TCRdist for better understanding protein structures and T cell recognition. Dr. George shares his journey from an MD/PhD background to his current research, offering advice for students interested in pursuing careers at the intersection of math, biology, and medicine to address complex challenges in cancer treatment.

Interview with Dr. Jacob Scott

Dr. Jacob Scott, a physician-scientist at the Cleveland Clinic, explains how his background in physics and time in the Navy eventually led him to medicine, where he now uses mathematical models to study cancer evolution and treatment resistance. He emphasizes the importance of moving beyond one-size-fits-all cancer therapies by using models informed by tumor biology and genomics. The conversation also covers the role of AI, game theory, and fitness landscapes in understanding cancer, along with Dr. Scott’s advice for students to stay curious, ask great questions, and pursue their passions.

Interview with Dr. Alexander "Sandy" Anderson

Dr. Alexander Anderson discusses how mathematical oncology uses models to better understand cancer dynamics and improve treatment strategies. He explains that cancer is not linear and that timing and sequencing of therapies—especially with immunotherapy—can drastically impact outcomes. The conversation highlights how spatial structure, evolutionary pressures, and patient-specific data all influence tumor behavior and response. Dr. Anderson also talks about the development of the Evolutionary Tumor Board, which uses modeling to personalize care in real time. Finally, he shares his motivation for starting Moffitt’s high school internship program to inspire the next generation of researchers in math and medicine