The body may be telling us something long before disease is diagnosed. Yan Gao, PhD, is developing new ways to detect those signals and better understand what they could reveal about cancer and other diseases.

Dr. Gao, Assistant Professor in the Data Science Institute’s Division of Biostatistics, finds deep satisfaction in uncovering the underlying mathematical and biological structures behind real-world problems. Her research uses statistical modeling and advanced computing to better understand how diseases develop over time and how treatments affect patients. By studying patterns in health data, she hopes to improve the way researchers track disease progression, evaluate treatments, and predict patient outcomes.

As a new member of the Cancer Center’s Discovery and Developmental Therapeutics Program, Dr. Gao is excited to expand the impact of her research and further explore how she can apply it to cancer and other chronic diseases.

Get to Know Dr. Gao

What is one big question driving your research?

One of the major questions driving my research is how we can better detect, model, and predict disease progression over time using longitudinal biomarkers and ultimately improve survival outcomes.

Many diseases, especially cancer, develop over years or even decades. Detecting changes early and intervening at the right time may help improve outcomes and slow disease progression.

For example, the immune system can gradually change over time because of factors such as aging, lifestyle, environmental exposures, and chronic stress. These changes may show up in biomarkers before a disease becomes clinically detectable. By studying how these biomarkers change over time, researchers may be able to identify early signs of disease, immune dysfunction, or treatment response in conditions such as cancer and infectious diseases.

Tell us about a project you’re working on right now.

I am currently developing new statistical methods to better understand how biomarkers change over time and how those changes relate to disease progression and survival.

One particularly exciting aspect of my research is its potential application to cancer research and chronic disease monitoring, where repeated biomarker measurements often contain important cumulative information that traditional approaches may overlook. My broader research agenda also includes developing scalable computational algorithms and interpretable statistical frameworks that can support clinical trials, translational oncology research, and individualized patient risk assessment. 

What opportunities are you looking forward to as a new Cancer Center member?

Cancer Center membership will strengthen interdisciplinary collaborations and expand the translational impact of my statistical methodological research in longitudinal biomarkers, survival analysis, surrogate endpoint evaluation, and clinical trial design. Close interactions with clinicians, translational researchers, and basic scientists are therefore essential for ensuring that methodological developments remain clinically meaningful and scientifically impactful. These collaborations may help facilitate the development of more statistically rigorous, computationally efficient, and clinically interpretable methodologies for precision oncology and cancer clinical trials.

Where do you see the field of biostatistics heading in the coming years?

I believe the field of biostatistics will become increasingly interdisciplinary, data-rich, and scientifically integrative. With the increasing availability of complex biomedical data, including electronic health records, genomic information, wearable device data, medical imaging, there is an increasing need for both applied statistical research and statistical methodological development that can integrate multiple complex data sources in scientifically meaningful ways. I expect applied biostatistics to become even more deeply integrated into clinical research, public health, and precision medicine, with strong emphasis on solving real-world biomedical problems.

How do you like to spend time outside of work?

I enjoy fitness dancing, strength training, yoga, and spending time outdoors. Because my daily work involves extensive analytical thinking and long hours of intellectual focus, physical activity has become an important way for me to maintain both physical and mental well-being. In the summer, I enjoy hiking and cycling, especially along the Chicago Lakefront Trail near Lake Michigan and the Oak Leaf Trail near Honey Creek. I have always liked lakes and natural environments because they feel peaceful and calming to me. I also enjoy visiting museums and cultural institutions, especially art, history, and science museums.

Learn more about Dr. Gao and see her full list of publications.