The world around us is neither totally regular nor completely random. Our and robots’ reliance on spatiotemporal patterns in daily life cannot be over-stressed, given the fact that most of us can function (perceive, recognize, navigate) effectively in chaotic and previously unseen physical, social and digital worlds. Data science has been promoted and practiced as an interdisciplinary  academic field for over 50 years (John Tukey) and the word “pattern recognition” is commonly used in machine learning for seeking patterns from data. The seminal work on Pattern Theory (Ulf Grenander) was the first to articulate the concept of patterns in data in precise mathematical terms, instead of algorithms only. Yet, what and where are the “ZEROS” in a pattern regularity space for real world datasets remains unspecified. In this talk, I will illustrate via various applications that, just as important as the use of numerical zero in the decimal Hindu-Arabic

numeral system (500 AD), a mathematically well-defined ZERO-state in multi-modality, multidimensional data regularity space exists and is computationally feasible to work with for pattern recognition tasks.

Dr. Liu is a professor of EECS at Penn State University (PSU), University Park, USA, trained in physics/EE, computer science and theoretical robotics/AI (B.S, China; Ph.D. USA; Postdoc, France). With an NSF (USA) research-education fellowship award, she spent one year at DIMACS (NSF center for DIscrete MAthematics and Theoretical Computer Science) before joining the faculty of the Robotics Institute, Carnegie Mellon University for ten years. Currently at PSU, she is the director of the Human Motion Capture Lab for Smart Health and co-directs the Lab for Perception, Action and Cognition (LPAC). Dr. Liu has been a visiting professor at Stanford University, ETH Zurich, Tsinghua University and Google/MSR/MSRA. She is on sabbatical 2023-2024 at CMU. A central theme of Dr. Liu’s research is on group theory-based “computational regularity” for multimodality data (funded continuously by US NSF, including a prestigious multidisciplinary INSPIRE grant) with diverse applications in robotics, human/machine perception, human activity in sports and in health. Dr. Liu chaired three international competitions at CVPR, ECCV, ICCV on Computer Vision algorithms for Detecting Symmetry in the Wild, and is the lead author for the book on “Computational Symmetry in Computer Vision and Computer Graphics”. Her industrial visits to Google Mountain View and Microsoft Silicon Valley/Uber resulted in two granted US patents. She has served as a program chair for Computer Vision and Pattern Recognition (CVPR) Conference 2017 and Winter Conference on Applications of Computer Vision (WACV) 2019, area chairs for all major computer vision/graphics conferences (CVPR/ECCV/ICCV/MICCAI/ACM MM/SIGGRAPH), and as an associate editor for IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI).