I’m very pleased to announce the release of our new book on the applications of machine learning in the Earth Sciences.
Here’s the link on Amazon
High-level recognition from NASA regarding our work in Vehicle Level Reasoning that appeared in the recent FY15 NASA Management and Performance Report (page 101):
“NASA and its partners established the technical feasibility of a vehicle-level prognostic reasoning system. This system would offer an onboard capability to provide additional warning about potential aircraft system failures that could degrade safety. The information could allow pilots or maintenance personnel to take necessary steps to ensure continued safe operation. The prognostic reasoning system combines onboard system measurements with a data mining capability to detect out-of-the-ordinary, or anomalous, conditions. Using this approach, the reasoning system compares onboard conditions with historical data linked to actual failures. Pilots and maintenance personnel would be notified when the system detects an anomaly. Designers used subject-matter experts during the development of this system to improve the system’s ability to detect potential problems correctly, while limiting the number of false detections. During the multi-year activity that completed in 2013, NASA and its partners used a prototype reasoning system to correctly predict three safety incidents from a set of regional airline flight data. The system also correctly detected and provided advanced warning of four faults that were injected into an aircraft navigation system. To improve opportunities for technology transfer, NASA and its partners tested the reasoning system using existing aircraft hardware, software, and communication protocols.”
Piscataway, New Jersey, USA, January 2014: Ashok N. Srivastava, Ph.D. from has been named an IEEE Fellow. He is being recognized for “leadership and contributions in data mining to enhance the safety of aerospace systems.”
The IEEE Grade of Fellow is conferred by the IEEE Board of Directors upon a person with an outstanding record of accomplishments in any of the IEEE fields of interest. The total number selected in any one year cannot exceed one-tenth of one- percent of the total voting membership. IEEE Fellow is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement.
The IEEE is the world’s leading professional association for advancing technology for humanity. Through its 400,000 members in 160 countries, the IEEE is a leading authority on a wide variety of areas ranging from aerospace systems, computers and telecommunications to biomedical engineering, electric power and consumer electronics.
Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards. The association also sponsors or co-sponsors nearly 400 international technical conferences each year. If you would like to learn more about IEEE or the IEEE Fellow Program, please visit www.ieee.org.
I’ve moved from NASA and am now the Chief Data Scientist at Verizon. I’m starting a new lab in downtown Palo Alto and we are hiring! If you are interested please send me a note.
We will be focusing on big data R&D in a number of key verticals. The data sets are massive and the opportunity is immense.
Earlier this month, Ashok Srivastava was elected to the grade of Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). AIAA Associate Fellows are individuals of distinction who have made notable and valuable contributions to the arts, sciences, or technology of aeronautics or astronautics. Dr. Srivastava will receive the honor during the AIAA Associate Fellow Dinner at the 51st AIAA Aerospace Sciences Meeting and Exhibit in January.
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly with contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community. Probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.
Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management.
Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors. The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems.
Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability.
The 2012 Conference on Intelligent Data Understanding (CIDU 2012) is organized under the theme of “Bringing Data and Models Together” and will attract top researchers and practitioners in the field of data mining focusing on applications to Earth & Environmental Systems, Space Science, and Aerospace & Engineering Systems. The Organizing Committee is soliciting theme-oriented papers that advance one of these areas through the use of data mining, machine learning, or computational intelligence techniques. We invite papers that include a clear link between the domain and analysis methods, and papers that give perspectives on methods to bring data-driven and model-based methods together are particularly sought. We also invite submission of 2-page extended abstracts for posters reporting new and interesting results, ideas, or work-in-progress.
All papers and posters will be peer-reviewed based on technical merit, significance, originality, relevance, and clarity. Papers should be no more than 8 pages and describe original work not previously published in a refereed conference or journal. The CIDU 2012 proceedings will be indexed by IEEE Xplore and DBLP. Selected papers will be invited to be extended for consideration in the journal Statistical Analysis & Data Mining.