Computational and Quantitative Medicine

The Department of Computational and Quantitative Medicine within the Beckman Research Institute, headed by Nagarajan Vaidehi, Ph.D., integrates various aspects of computational medicine. It is charged with building cross-disciplinary collaborations and developing better diagnostic tools, improved personalized therapies and more efficient clinical trials, consistent with the mission of City of Hope.
The department is an integral part of multi-disciplinary disease-related research teams within the Beckman Research Institute, the Diabetes & Metabolism Research Institute and the comprehensive cancer center. It utilizes quantitative science methods – including mathematics, physics, statistics and engineering – to conduct medical research and train the next generation of investigators.

The Department Consists of Three Divisions:

Division of Biostatistics

Headed by Joycelynne Palmer, Ph.D., the Division of Biostatistics is home to the statisticians of the Biostatistics and Mathematical Oncology Core, a shared resource of the City of Hope’s comprehensive cancer center. The division encourages the collaboration of statisticians in cancer, diabetes and other research areas, and promotes statistics and epidemiology research, education and service.

Division of Health Analytics

Headed by James Lacey, Ph.D., M.P.H., the goal of the Division of Health Analytics is to generate insights that enable data-driven decisions in research, clinical care and public health. The division uses broad and diverse data, technologies, analytic methods, strategies and partnerships to ask questions, tackle problems and create knowledge. The division conducts innovative and hypothesis-driven population-health informatics and research projects, and collaborates with other stakeholders across the enterprise to improve research, clinical and business outcomes.

Division of Mathematical Oncology

Headed by Russell Rockne, Ph.D., the Division of Mathematical Oncology is focused on patient-specific mathematical models of cancer growth and response to therapy. This research lies at the interface of mathematics, imaging, histopathology and genomics, combining information to mathematically model treatment of individual cancer patients, and how their tumors evolve when confronted with specific treatment regimens.


Vaidehi Lab

The Vaidehi lab develops physics-based computational methods to study the structure, function and dynamics of proteins and protein complexes.

Zhaohui Gu Lab

The Zhaohui Gu lab will combine the expertise of computational biology and functional genomics to further advance our understanding of leukemia heterogeneity and evolution.