Dr. Charles Gawad's lab works at the interface of biotechnology, computational biology, cellular biology, and clinical medicine to develop and apply new tools for characterizing genetic variation across single cells within a tissue with unparalleled sensitivity and accuracy. They are focused on applying these technologies to study cancer clonal evolution while patients are undergoing treatment with the aim of identifying cancer clonotypes that are associated with resistance to specific drugs so as to better understand and predict treatment response.

Home Department: Electrical Engineering
Faculty Advisor: Leonidas Guibas
Talk Title: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Event: 2017 Institute of Electrical and Electronics Engineers (IEEE) Conference on Computer Vision and Pattern Recognition

Awarded in 2016
Talk Title: Volumetric and Multi-View CNNs for Object Classification on 3D Data
Event: Conference on Computer Vision and Pattern Recognition (CVPR) 2016

Awarded in 2015
Talk Title: Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Event: International Conference on Computer Vision (ICCV) 2015