Catalyzing and accelerating innovation in cancer data science and increasing capacity for data-driven cancer research and evidence-based cancer care.
The Center for Cancer Data Science (CCDS), founded through a partnership between the Penn Institute of Biomedical Informatics and the Abramson Cancer Center, is dedicated to advancing data-driven cancer research and transforming prevention and care. Serving as a collaborative hub for data scientists across Penn and the Children’s Hospital of Philadelphia (CHOP), the Center expands capacity for innovative, evidence-based approaches that accelerate cancer science and improve patient outcomes.
Advances in technology have enabled the Center for Cancer Data Science to generate massive, rich health data in research and as part of healthcare delivery. Data from electronic health records (EHR), -omics, imaging and mobile health (mHealth), among other areas, promise to greatly advance precision health and to transform our learning health system. Yet they are also highly complex and present significant analytical challenges.
Cancer data science is broadly defined as transdisciplinary; biostatistics, bioinformatics, biomathematics, computational biology, computer science and other relevant quantitative science pursuits play important roles. The field focuses, in particular, on the development and application of statistical and ML/AI methods and theory for the analysis of rich, complex health data (e.g. registry, claims, EHRs, -omics, imaging, mHealth, and clinical trials data) that continue to proliferate in cancer research and cancer care.
The Center for Cancer Data Science seeks to accelerate innovation by harnessing the full power of rich, complex health data, and exerting sustained, powerful impact on cancer research, prevention and care through a transdisciplinary, team-science approach.
Explore the latest CCDS research updates and collaborations shaping the future of cancer data science and patient outcomes.
Drs. Qi Long and Robert Gross have received a $6 million NIH/NIMH R01 grant to develop multimodal AI tools that enhance HIV prevention, care, and clinical decision-making.
Qi Long, PhD, has been elected to the 2026 Class of AIMBE Fellows, one of the highest professional honors in medical and biological engineering.
Penn researchers have received NIH/NIBIB funding to develop robust and interpretable multi-modal AI models that integrate genomics, imaging, and electronic health data to advance cancer and Alzheimer’s disease research.
A new method helps prevent “preference collapse” in AI training, offering a fairer, more balanced approach to aligning large language models.
Penn researchers have developed an innovative approach for detecting AI-generated text that remains accurate even after human edits.
Dr. Qi Long and collaborators have received NIH/NIBIB funding to advance neurosymbolic AI for smarter, more trustworthy healthcare.
Qi Long, Professor at the University of Pennsylvania, has been named a Fellow of the Institute of Mathematical Statistics—a prestigious honor awarded for his impactful work in advancing data science and applying statistics to improve biomedical research.
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