Piero Bonissone


Dr. Bonissone is an independent consultant specialized in the use of analytics for Industrial Internet applications to Oil and Gas services. From Dec 2014 to January 2017 he was an Advanced Analytics Adviser to Schlumberger (SLB), where he played a key role in SLB Digital Transformation, such as BOP PHM support, part forecasting and market intelligence, and many PHM projects related to equipment reliability. He provided consulting in machine learning applications, ranging from project definition, risk abatement, project evaluation, transition from development to deployment, and model maintenance. In the past two years he was also a consultant for DIGILE, a Finnish startup, and Ford Motor Co.

On April 1, 2014 he retired from his position of Chief Scientist and Coolidge Fellow at GE Global Research, where he worked over the past 34 years. He is a pioneer in the application of machine learning, fuzzy logic, Artificial Intelligence (AI), soft computing, computational intelligence and approximate reasoning systems. His most recent interests are: (1) development of prognostics and health management (PHM) algorithms for fleets of assets; (2) multi-criteria decision making systems for logistics and financial applications; and (3) the automation of the life-cycle of intelligent systems. His latest research focus is dynamic predictive model ensemble and fusion for cloud-based services.


He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for the Advancement of Artificial Intelligence (AAAI). He served as Editor in Chief of the International Journal of Approximate Reasoning for 13 years. Dr. Bonissone is very active in the IEEE, serving as President of the IEEE Neural Networks Society (now Computational Intelligence Society) as well as a member of the IEEE Technical Board Activities (TAB).

Specialties: Machine Learning, Ensemble Learning, Data Mining, Computational Intelligence (Fuzzy Logic, Neural Networks, Evolutionary Algorithms), Soft Computing, Multi-Criteria Decision Making, Multi-Objective Optimization, Anomaly Detection, Fault diagnostics and prognostics, Multiple Classifier Systems, Machine Learning, Data Mining, Artificial Intelligence (Plausible Reasoning, Probabilistic Reasoning, Case Based Reasoning)


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