This talk covers an overview of how statistical machine learning has been successfully applied to challenging anomaly detection problems involving noisy sensor signals in real customer use-cases including the oil & petroleum, automotive, semiconductor, maritime, the cement industries.
This talk is based on a customer presentation for an automotive company in 2019.
Dr. Tsuyoshi ide ("Ide-san") is a Senior Technical Staff Member with IBM T. J. Watson Research Center, New York, USA. He received his Ph.D. from the University of Tokyo in condensed matter physics in 2000. Since around 2003, he has been working on data mining and machine learning research through a variety of real-world applications. Currently, he is part of the Trusted AI group in IBM Research. His recent research interests include explainable AI, anomaly detection, tensors, and collaborative learning. For more detail, see his website: http://ide-research.net/