Qin Ma, PhD
What do you research, and what brought you to that area of study?
I lead a biomedical informatics and AI research program focused on developing advanced methods to analyze multimodal data (e.g., single-cell and spatial omics data). My work integrates graph representation learning strategies to better understand molecular and cellular mechanisms of human diseases, such as immuno-oncology and neurodegenerative diseases. In addition, we have been developing and deploying biomedical foundation models to enable integration of omics data, clinical imaging data, and a variety of other health data to support the clinical decision-making process. My training in applied mathematics and computational biology naturally led me to this field. I have always been interested in solving complex biological problems using quantitative and computational approaches, especially when those methods can directly support biomedical discovery and translational research.
What impact do you hope your research will have?
I am especially motivated by work that translates advanced computational methods into practical resources that can be widely used by the research community. I hope my research helps bridge the gap between large-scale biomedical data and meaningful biological or clinical insight. By building interpretable and scalable AI tools, my goal is to enable researchers and clinicians to better understand disease mechanisms, identify therapeutic targets, and ultimately support more precise and personalized approaches to medicine.
What excites you about doing interdisciplinary research at OSU?
OSU provides an exceptional environment for interdisciplinary collaboration. By working closely with clinicians, basic scientists, statisticians, and data scientists, we can tackle fundamental biomedical problems that no single discipline could solve independently. Specifically, the culture of collaboration across institutes like CBI, the Pelotonia Institute for Immuno-Oncology, Clinical and Translational Science Institute, and the Translational Data Analytics Institute makes it possible to move ideas quickly from methodology development to real-world biomedical applications.
What are you most proud of?
I am most proud of the professional growth and achievements of the trainees and junior faculty I have mentored, and of the collaborative teams I have helped build. Seeing them grow into independent scientists and leaders is incredibly rewarding, and it reflects the collaborative and supportive culture we strive to create in our research programs.