Geometric data and signal processing have made remarkable progress in recent years, and the advent of modern AI tools promises an even brighter future. Many classical and contemporary methods, however, use vast amounts of data, substantial processing power, and considerable natural resources to achieve their results. These approaches can be expensive, environmentally harmful, and ultimately unsustainable. This talk explores efforts to do more with less in geometry processing by ensuring that methods use all the information contained in input data, by maximally utilizing noisy and sparse data, and by developing methods for computer-aided fabrication with sustainable materials. We will dive deeper into surface digitization from extremely low-resolution scans, training generative AI models without collecting terabytes of data, denoising functions on low-quality geometric domains, developing robust geometric algorithms with mathematical guarantees, and design tools for alternative manufacturing methods.
Bio: Oded Stein received his PhD from Columbia University in Applied Mathematics from Columbia University under the supervision of Eitan Grinspun for his research of smoothness energies in geometry processing in 2020. From 2020 to 2022 he was a postdoctoral fellow at MIT’s Computer Science & Artificial Intelligence Lab with the gracious support of the Swiss National Science Foundation’s Early Postdoc. Mobility fellowship. Since 2023 Oded Stein is an Assistant Professor at USC’s Viterbi School of Engineering, supported by the Powell Faculty Research Award and the National Science Foundation.