Recent advances in artificial intelligence have led to significant progress in our ability to extract information from images and time sequences. Maintaining this rate of progress hinges upon attaining equally significant results in the processing of more complex signals such as those that are acquired by autonomous systems and networks of connected devices, or those that arise in the study of complex biological and social systems. This award establishes FINPenn, the Center for the Foundations of Information Processing at the University of Pennsylvania. The focus of the center is to establish fundamental theory to enable the study of data beyond time and images. The center's premise is that humans' rich intuitive understanding of space and time may not necessarily be applicable to the processing of complex signals. Therefore, matching the success in time and space necessitates the discovery and development of foundational principles to guide the design of generic artificial intelligence algorithms. FINPenn will support a class of scholar trainees along with a class of visiting postdocs and students to advance this agenda. The center will engage the community through the organization of workshops and lectures and will disseminate knowledge with onsite and online educational activities at the undergraduate and graduate level.
FINPenn builds on two observations: (i) To understand the foundations of data science it is necessary to succeed beyond Euclidean signals in time and space. This is true even to understand the foundations for Euclidean signal processing. (ii) Humans live in Euclidean time and space. To succeed in information processing beyond signals with Euclidean structure, operation from foundational principles is necessary because human intuition is of limited help. For instance, convolutional neural networks have found success in the processing of images and signals in time but they rely heavily on spatial and temporal intuition. To generalize their success to unconventional signal domains it is necessary to postulate fundamental principles and generalize from those principles. If the generalizations are successful they not only illuminate the new application domains but they also help establish the validity of the postulated principles for Euclidean spaces in the tradition of predictive science. The proposers further contend that the foundational principles of data sciences are to be found in the exploitation of structure and the associated invariances and symmetries that structure generates. The initial focus of the center is in advancing the theory of information processing in signals whose structure is defined by a group, a graph, or a topology. These three types of signals generate three foundational research directions which build on the particular strengths of the University of Pennsylvania on network sciences, robotics, and autonomous systems which are areas in which these types of signals appear often.
This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
