Recent advances in machine learning for industrial sensor data

Ide-San Ide


Sensor data analytics is one of the major application fields of data mining and machine learning. Typically taking real-valued time-series data from physical sensors as the input, its problem setting includes a variety of tasks depending on the application domain, not limited to the traditional regression and classification.

This talk will first introduce technical challenges in industrial sensor data analytics. Then it will cover recent developments in machine learning algorithms in sensor data analytics. Major topics include change detection using directional statistics and multi-task extension of graph-based anomaly detection.

This talk is based on recent invited talks:

(1) Department Seminar, Department of Computer Science (January 10, 2018), University at Albany, State University of New York, Albany, USA.

(2) The 12th ICME International Conference on Complex Medical Engineering (CME 2018, September 6-8, 2018), Shimane, Japan.

(3) IEEE International Workshop on Data Mining for Service (DMS 2017, November 18, 2017), New Orleans, USA.


Photo of Ide-San Ide

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:

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