BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH BEGIN:VEVENT UID:20260307T150950-1677837181-northwestern.edu DTSTAMP:20260307T150950 DTSTART:20260302T120000 DTEND:20260302T130000 SUMMARY:CS Seminar: Data-Driven Neural Mesh Editing – without 3D Data (Rana Hanocka) LOCATION:3514, Mudd Hall ( formerly Seeley G. Mudd Library) DESCRIPTION:Monday / CS Seminar\nMarch 2 / 12:00 PM\nHybrid / Mudd 3514\nSpeaker\nRana Hanocka, University of Chicago\nTalk Title\nData-Driven Neural Mesh Editing - without 3D Data\nAbstract\nMuch of the current success of deep learning has been driven by massive amounts of curated data, whether annotated or unannotated. Compared to image datasets, developing large-scale 3D datasets is either prohibitively expensive or impractical. In this talk, I will present several works that harness the power of data-driven deep learning for tasks in shape editing and processing, without any 3D datasets. I will discuss works that learn to synthesize and analyze 3D geometry using large image datasets.\nBiography\nRana Hanocka is an Assistant Professor at the University of Chicago and holds a courtesy appointment at the Toyota Technological Institute at Chicago (TTIC). She founded and directs the 3DL (Threedle) research collective, comprised of enthusiastic researchers passionate about 3D, machine learning, and visual computing. Her research interests span computer graphics, computer vision, and machine learning. She completed her Ph.D. at Tel Aviv University under the supervision of Daniel Cohen-Or and Raja Giryes. Her Ph.D. research focused on building neural networks for irregular 3D data and applying them to problems in geometry processing.\n---\nZoom: TBA\nPanopto: TBA\n\nPiP URL: https://planitpurple.northwestern.edu/event/638816 END:VEVENT END:VCALENDAR ORGANIZER:Department of Computer Science (CS)