Fall 2017 Biweekly Seminar Series – September 27, 2017 – Dr. Yuriy Zinchenko

Speaker: Dr. Yuriy Zinchenko, University of Calgary
Location: Mathematical Sciences 325
Date: Wednesday, September 27, 2017
Time: 1:00 PM – 2:00 PM

Title: Radiotherapy Optimization: From Uncertainty to DVH Modeling

Abstract: Radiation therapy is an important modality in cancer treatment. To find a good treatment plan, optimization models and methods are typically used. Within the optimization models, several conflicting objectives such as sparing of healthy Organs at Risk (OAR) and eradicating the tumor, are pursued simultaneously. Besides the inherent complexity of some of the clinically-important planning criteria, particularly, the so-called dose-volume requirements for the OAR, the treatment optimization process is further complicated by the presence of uncertainties. In this talk, we will briefly survey the optimization approaches that can handle the uncertainties by robustifying the underlying model and discuss several alternatives to approximate the exact dose-volume requirements in a computationally-tractable fashion.

More about Dr. Zinchenko:

Dr. Zinchenko received his Ph.D. from Cornell University in 2005 under the supervision of Prof. James Renegar. From 2005 to 2008 he was a post-doctoral fellow at the Advanced Optimization Lab at McMaster University, working with Prof. Tamas Terlaky and Prof. Antone Deza. Additionally, from 2006 to 2008 Dr. Zinchenko was a post-doctoral researcher with the Radiation Oncology group at the Princess Margaret Hospital in Toronto. Currently, Dr. Zinchenko is an Associate Professor of Mathematics and Statistics at the University of Calgary. Dr. Zinchenko’s primary research interest lies in convex optimization, and particularly, the curvature of the central path for interior-point methods, and applications. In 2007, Dr. Zinchenko’s work on optimal radiotherapy design was recognized by the MITACS Award for Best Novel Use of Mathematics in Technology Transfer. In 2012-2015 he served as one of the PIs for PIMS Collaborative Research Group grant on optimization.


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