Department of Computer Science and the College
Rana Hanocka’s research is focused on building artificial intelligence for 3-D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep learning, the most popular form of artificial intelligence, has unlocked remarkable success on such structured data as text, images, and video. She is interested in harnessing the potential of these techniques to enable effective operation on unstructured 3-D geometric data. She has contributed to the development of a convolutional neural network designed specifically for meshes, and has also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation), as well as broader applications related to these areas.
Hanocka’s research has been published in such premier computer graphics/vision venues as the Conference on Computer Vision and Pattern Recognition (CVPR), and the Association for Computing Machinery’s ACM Transactions on Graphics and the ACM Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH). She received the 2020 Dan David Prize in Artificial Intelligence, was selected as a rising star in electrical engineering and computer sciences by the University of California, Berkeley, and was awarded the Outstanding Data Science Fellowship by Israel’s Council for Higher Education.
She earned a BSc in electrical engineering at Rensselaer Polytechnic Institute, followed by MSc and PhD degrees in electrical engineering from Tel Aviv University.