State University of New York
Stony Brook, New York
Distinguished Professor, Computer Science and Applied Mathematics/Statistics
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 for cancer.
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 developed a way to analyze large amounts of data of multiple types and meaningfully correlate them across data types, for example, radiomic features from CT scans and gene expression from the same tumor samples. The team has also applied a new mathematical tool to extract more refined disease subtypes from data from the two largest genomic breast cancer studies, METABRIC in the UK and The Cancer Genome Atlas (TCGA) in the US. Lastly, the team has developed a deep learning method to enhance outcomes prediction. The method was tested with data from a large study of multiple myeloma and ten major cancers in TCGA and showed superior predictive performance compared to other alternative methods.
In the coming year, the team will continue to apply their mathematical tools to myriad areas of cancer research, including refining and characterizing features of a novel and particularly lethal breast cancer subtype, predicting treatment response, testing whether CT scans correlate with tumor immune status, and improving methods for analyzing pathology slides and radiological images.
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.
The Simons Foundation Award
Memorial Sloan Kettering Cancer Center
New York, New York
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