Company Filing History:
Years Active: 2014
Title: Innovations by Ping Wang in Causal Analysis
Introduction
Ping Wang is an accomplished inventor based in Atlanta, GA. He has made significant contributions to the field of causal analysis through his innovative methods for retrieving and classifying causal sets of events from unstructured signals. His work is particularly relevant in the context of visual data processing and analysis.
Latest Patents
Ping Wang holds a patent for "Systems and methods for retrieving causal sets of events from unstructured signals." This patent describes a method that enhances the performance of retrieving and classifying causal sets of events from unstructured signals. The method involves applying a temporal-causal analysis to the unstructured signal, which includes representing the occurrence times of visual events as a set of point processes. The patent outlines how visual codewords produced by a space-time-dictionary representation of unstructured video sequences can be interpreted as point processes. Furthermore, it discusses obtaining a nonparametric estimate of the cross-spectrum between pairs of point processes and applying a spectral version of the pairwise test for Granger causality to identify interaction patterns.
Career Highlights
Ping Wang is affiliated with the Georgia Tech Research Corporation, where he continues to advance his research in causal analysis and signal processing. His innovative approaches have garnered attention in the academic and research communities, contributing to the understanding of visual event interactions.
Collaborations
Throughout his career, Ping Wang has collaborated with notable colleagues, including James Rehg and Karthir Prabhakar. These collaborations have further enriched his research and expanded the impact of his work in the field.
Conclusion
Ping Wang's contributions to the field of causal analysis through his innovative patent demonstrate his expertise and commitment to advancing technology. His work continues to influence the way unstructured signals are analyzed and understood.