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Allen Tannenbaum, PhD

Distinguished Professor, Computer Science and Applied Mathematics/Statistics
State University of New York
Stony Brook, New York

Current Research

  • Seeking to develop new tools to interpret and understand large sets of scientific data.

  • Novel mathematical methods are applied to understand cancer growth, progression and response to therapy.

  • These efforts will inform novel treatment approaches that can be tested in the laboratory and ultimately in the clinic.


Certain characteristics of cancer are highly mathematical. For example, cancer growth itself can be modeled using relatively simple mathematical equations. Intracellular signaling is a very complicated phenomenon, but with mathematical models it can be understood as a network of connected nodes. The research led by Drs. Deasy, Tannenbaum, and Norton is applying an integrated mathematical approach to model cancer development and response to treatments.

Full Research Summary

Massive amounts of scientific data have been generated in cancer research over the last decade. The information that is buried in these data silos has the potential to both unlock new discoveries that will advance our knowledge of cancer initiation, treatment, and control, and to change the course of cancer care for millions of people around the globe. Appropriate mathematical models and tools need to be developed to shine a light on the inner principles driving cancer and treatment response. 

This project headed by Drs. Deasy, Tannenbaum and Norton brings together mathematicians, biologists, oncologists and other scientists to develop new tools to interpret, model, and understand scientific data. 

Novel mathematical methods will be used to focus on disease evolution, cancer treatment response, and variable patient risk of toxicity to advance precision medicine in cancer. 

The researcher team has already assessed the impact of genes that are likely associated with individual drug response, successfully identifying very plausible biological processes associated with drug response across various cell lines and cell types. 

In the coming year they will continue to refine their model to more accurately approximate what happens inside of a cell. They will test the effects of various treatments in their model of cancer cell function and then validate them in cell and biological systems. 

The mathematical methods derived from these efforts will be used to predict novel treatment approaches that can be tested in the laboratory and ultimately in the clinic.


Allen Tannenbaum 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.

Allen Tannenbaum

BCRF Investigator Since


Donor Recognition

The Henry and Marilyn Taub Foundation Award in Memory of Sandra Taub

Area(s) of Focus