The IEEE International Conference on Data Mining has awarded our work in Discovering Precursors to Aviation Safety Incidents and Accidents as a Top 10 Data Mining Case Study in the world. This citation represents work that comprises both novel algorithms for anomaly detection and predictive modeling as well as applications in the aviation domain.
Here is an article that recently appeared in Flight International about some novel data mining and machine learning techniques that we are developing to potentially detect pilot fatigue. We are using anomaly detection techniques as well as predictive methods to look for objective indicators of fatigue. This is ongoing work between a number of groups within NASA and outside partners.
The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2010 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.
I’ll be giving an invited talk on Discovering Precursors to Aviation Safety Incidents and participating on a panel on the Next Generation of Transportation Systems: Greenhouse Emissions and Data Mining. We also have a paper on Multiple Kernel Learning for Heterogeneous Anomaly Detection.
The Workshops on Algorithms for Modern Massive Data Sets (MMDS) will address algorithmic, mathematical, and statistical challenges in modern large-scale data analysis. The goals of this series of workshops are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets, and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote cross-fertilization of ideas.