The Optimal Partial Tumor Resection Problem
Taghi Khaniyev, Department of Industrial Engineering, Bilkent University
Taghi Khaniyev is an assistant professor at Bilkent University, Department of Industrial Engineering since 2021. He is also affiliated with UMRAM (National Magnetic Resonance Research Center) and Aysel Sabuncu Brain Research Center. He acquired his PhD in Management Science at the University of Waterloo in 2018. Prior to joining Bilkent, he was a postdoctoral fellow at MIT Sloan School of Management working in collaboration with Massachusetts General Hospital (MGH) on hospital operations management. His research in the healthcare domain focuses on developing and implementing data-driven analytical tools with the interplay of machine learning and optimization to predict outcomes of interest and prescribe personalized interventions for facilitating favorable outcomes. His main research interests are hospital operations management, deep neural networks, data-driven optimization, structure detection and decomposition in mathematical programs, and brain connectivity networks. His research has been published in prestigious scientific journals such as INFORMS Journal on Computing, European Journal of Operations Research, and Frontiers in Neuroscience; an algorithm he developed for decomposition and parallel processing of large-scale optimization problems have been adopted by the software company SAS Inc., the machine learning tool he developed for discharge prediction has become an integral part of the Capacity Coordination Center’s workflow at MGH (Harvard), a surgery duration prediction model he developed was used by Lucile Packard Children’s Hospital (Stanford), and his paper on the network optimization approach for identifying the hub regions in the human brain won the best student paper award by Canadian Operations Research Society (CORS).
Friday, November 18, 2022, 4.00 pm - IE Building, Blue Auditorium