Titles and Affiliations

Distinguished Professor, Computer Science and Applied Mathematics/Statistics

Research area

Developing new mathematical tools to interpret and understand large sets of data in order to gain a deeper understanding of cancer.


Mathematical approaches can be used to gain insight into how complex, interacting systems drive cancer, how cancer affects a patient, and how treatments affect cancer. As part of the Mathematical Oncology Initiative, Drs. Tannenbaum and Deasy have assembled a team of mathematicians, biologists, oncologists, and other scientists to develop mathematical models and tools that can be used to interpret many kinds of data. These tools can help us gain a deeper understanding of the overall picture of cancer, including areas such as disease evolution, treatment response, identifying subtypes, patient risk of toxicity, and more. Their work will contribute to the advancement of precision medicine in cancer.

Progress Thus Far

Drs. Tannenbaum and Deasy have successfully developed and applied advanced mathematical methods to complex datasets and made significant progress in addressing questions in several areas of cancer biology and treatment. They have described the interactions between hundreds of genes to study the impact of genetic alterations that occur during cancer evolution. In cases of ovarian cancer where these gene networks were perturbed, the disease tended to be the most lethal. The team was also able to use machine learning techniques to analyze features of cancer cells from the data available in the Genomics of Drug Sensitivity in Cancer database and demonstrated that it is possible to predict, for a specific cancer, how effective a certain cancer drug is likely to be. 

What's next

In the coming year, the team will continue to apply their mathematical tools developed thus far to myriad areas of cancer research, including refining cancer subtypes, predicting treatment response, and identifying genes that interact to promote cancer progression.


Allen Tannenbaum, PhD is an applied mathematician and presently Distinguished Professor of Computer Science and Applied Mathematics & Statistics at the State University of New York at Stony Brook. He is also Visiting Investigator of Medical Physics at Memorial Sloan Kettering Cancer Center in New York City. Dr. Tannenbaum has done research in numerous areas including robust control, computer vision, and medical imaging, having more than 500 publications. He pioneered the field of robust control with the solution of the gain margin and phase margin problems. He was one of the first to introduce partial differential equations in computer vision and biomedical imaging, co-inventing an affine-invariant heat equation for image enhancement. Tannenbaum and collaborators further formulated a new approach to optimal mass transport theory. In recent work, he has developed techniques using graph curvature ideas for studying cancer networks. His work has won several awards including IEEE Fellow, O. Hugo Schuck Award of the American Automatic Control Council, and the George Taylor Award for Distinguished Research.

BCRF Investigator Since


Donor Recognition

The Simons Foundation Award

Areas of Focus