When:
Monday, January 12, 2026
12:00 PM - 1:00 PM CT
Where: Mudd Hall ( formerly Seeley G. Mudd Library), 3514, 2233 Tech Drive, Evanston, IL 60208 map it
Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students
Cost: free
Contact:
Wynante R Charles
(847) 467-8174
wynante.charles@northwestern.edu
Group: Department of Computer Science (CS)
Category: Academic
Monday / CS Seminar
January 12 / 12:00 PM
Hybrid / Mudd 3514
Speaker
Rana Hanocka, UChicago
Talk Title
Data-Driven Neural Mesh Editing – without 3D Data
Abstract
Much 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.
Biography
Rana 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.
Research Interests: Computer Graphics, Computer Vision, Machine Learning