Exploiting Emerging Sensing Technologies Towards Structure in Data to Enhance Machine Perception

Talk Abstract: Structure in data can be leveraged to enhance learning. In many perception tasks, the embedded signals arising from physical processes of interest naturally have structure of high semantic relevance. However, traditional forms of remote sensing (e.g., vision) preserve such structure only in limited ways. This talk examines how embedded, distributed, form-fitting sensing, referred to as physically-integrated (PI) sensing, can preserve such structure in richer ways. While the analysis is agnostic to the particular technology for PI sensing, for which a range of options is emerging, especially driven by the Internet of Things, a particular emerging technology called Large-Area Electronics (LAE) is considered. Using synthetic data from 3D modeling and rendering of human-activity scenes, LAE-based PI sensing and vision-based remote sensing are emulated and perception systems are formed, showing: (1) enhanced data-efficiency of learning models based on PI sensing; (2) potential for selective deployment of PI sensors in new perception tasks, thanks to robust ranking of their value in such tasks; (3) enhanced data-efficiency of learning models based on vision sensing, by integrating PI sensing; (4) efficient mapping of PI-sensing features across perception tasks to enhance transferability of learning.

Murat Ozatay Bio: Murat Ozatay received the B.Sc. degree in electrical and electronics engineering from Middle East Technical University, Ankara, Turkey in 2015, and the M.A. degree in electrical engineering from Princeton University, Princeton, NJ in 2017, where he is currently pursuing his Ph.D. degree. He is a member of Verma Lab. His research focuses on bringing together algorithms and insights for learning with technologies and systems for advanced sensing. His primary research interests include machine learning, artificial intelligence, Internet-of-Things, and the design of VLSI systems.