Geometric Approaches for Robust Machine Learning
Channel: Open Research Institute Inc.
Date: 2023-07-11

Many physical phenomena — such as motion, shape, and light — when captured through sensors, result in complex, high-dimensional spatiotemporal signatures that are difficult to analyze and interpret. The sources of difficulty are many: non-trivial transformations required for inference, physical variabilities that affect data without altering the essence, and complex physical constraints on environments. Understanding and accounting for these issues is ever more important in low-data and high-stakes applications. In this talk, we present an overview of our research, where we have been advancing the use of geometry and topology to advance robustness and interpretability of machine learning in these types of scenarios. Our application areas span imaging, wearables, and interactive media.

Biography:

Pavan Turaga is a Professor in the School of Arts, Media and Engineering, and the School of Electrical, Computer and Energy Engineering at Arizona State University. Their research spans representational foundations for structured and unstructured data, including time-series, image, video, 3D point clouds and more, and their applications in areas such as interactive systems, machine learning, computer vision, and mobile-health.