Princeton Robotics Seminar

The series launches in Spring 2021, first featuring Princeton faculties virtually. It is scheduled on Thursdays at 3-4:30PM EST and available to the public. Please join the Zoom meeting with this link(link is external).

Mar 11, 2021 - Naveen Verma(link is external), Princeton ECE

AI Meets Large-scale Sensing: preserving and exploiting structure of the real world to enhance machine perception

Abstract: Machine capability has reached an inflection point, achieving human-level performance in tasks traditionally associated with cognition (vision, speech, strategic gameplay).  However, efforts to move such capability pervasively into the real world, have in many cases fallen far short of the relatively constrained and isolated demonstrations of success. A major insight emerging is that structure in data can be substantially exploited to enhance machine learning. This talk explores how the statistically-complex processes of the real world can be addressed by preforming sensing in ways that preserve the rich structure of the real world. This evokes questions like: what sort of structure is useful; what sort of models can exploit such structure; what sensing technologies enable this structure; what computational architectures are required for this? These questions are investigated, and promising approaches from our on-going research are presented, spanning technologies for large-scale, form-fitting sensing, mixed-signal architectures for in-memory computing, and machine perception models for sensor fusion.

Bio: Naveen Verma received the B.A.Sc. degree in Electrical and Computer Engineering from the UBC, Vancouver, Canada in 2003, and the M.S. and Ph.D. degrees in Electrical Engineering from MIT in 2005 and 2009 respectively. Since July 2009 he has been a faculty member at Princeton University, where he is also currently director of the Keller Center for Innovation in Engineering Education. His research focuses on advanced sensing systems, exploring how systems for learning, inference, and action planning can be enhanced by algorithms that exploit new sensing and computing technologies. This includes research on large-area, flexible sensors, energy-efficient statistical-computing architectures and circuits, and machine-learning and statistical-signal-processing algorithms. Prof. Verma has served as a Distinguished Lecturer of the IEEE Solid-State Circuits Society, and currently serves on the technical program committee or advisory board for ISSCC, VLSI Symp., and IEEE Signal-Processing Society (DISPS).