Gedas is currently a first year PhD student in the CIS Department at University of Pennsylvania. His research focuses on applying machine learning techniques to solve computer vision problems. Specifically, he is interested in using deep learning and spectral graph theory for object-level boundary detection and semantic shape recognition. He is also interested in developing methods that utilize low-level boundary information for higher-level vision tasks such as object-detection. His advisor is Professor Jianbo Shi and he is supported by NSF IGERT Fellowship. In 2014, Gedas received his Bachelor's Degree from Dartmouth College where he studied Computer Science and Mathematical Finance. While at Dartmouth, he worked on various computer vision and machine learning research projects at Visual Learning Group. His advisor at that time was Professor Lorenzo Torresani.