University of California, Berkeley
Electrical Engineering and Computer Science
Sole Owner at Piero P Bonissone Analytics, LLC
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Electrical Engineering and Computer Science
Dr. Bonissone is an independent consultant specialized in the use of analytics for Industrial Internet applications. 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.
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)
I provide consulting services in machine learning (ML) and other analytic applications. My services range from training courses in ML and Computational Intelligence to project definition and risk abatement, project evaluation, transition from development to deployment, and model maintenance.
Every Fall I taught a course on "Fuzzy Logic and Computational Intelligence" either in the ECSE Dept. or in the DSES Dept.
Since 1982, I supervised 5 phD theses and 34 MS theses
I spent 1 year at the Research Institute on AI (IIIA), in Barcelona, as part of the sabbatical year associated with the GE Coolidge Fellowship that I received in 1993.
Electronics Research Lab, EECS Dept
I have been VP of Finances and President of the IEEE Neural Networks Council (NNC), Neural Networks Society (NNS) and Computational Intelligence Society (CIS) for the past twenty years. Currently I am a member of the IEEE CIS Advisory Committee
Click on the USPTO URL to access all patents.
Below is a list of the eleven most recent issued patents.
A system for at least a partial underwriting of insurance policies is described. Various rules are created, along with a degree of satisfaction for each rule. Rules may be directed toward various insurance underwriting components (e.g., cholesterol levels, blood pressure, etc.). Based on the degree of satisfaction for each rule, a component may be assigned to a category. Based on the category for each component, the insurance application may be assigned an underwriting category.
A semi-automated method for interactively analyzing a patent landscape in one embodiment includes retrieving a plurality of relevant patents indicative of a predetermined conceptual region of the patent landscape from a patent repository using a query. Competitive analysis of the plurality of relevant patents is conducted using an interactive network-based visualization technique. The competitive analysis is used for intellectual property enforcement, due diligence, and strategic investment analysis.
A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; committing the initial population of solutions to an initial population archive; performing a multi-objective process, based on the initial population archive and on multiple competing objectives, to generate an efficient frontier, the multi-objective process including a evolutionary algorithm process, the evolutionary algorithm process utilizing a dominance filter, the efficient frontier being used in investment decisioning.
A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
A method for advanced condition monitoring of an asset system includes monitoring a variable of an asset system using the at least one sensor of a smart sensor system; determining whether the asset system has departed from normal operation; and identifying the variable of the asset system indicating the departure from normal operation. In another method, the time sequential values of the monitored variable is analyzed by using a Rank Permutation Transformation test, a Hotelling's T.sup.2 statistic test, and a Likelihood Ratio Test; and a change of an operating condition of the asset system is determined using the analyzed values. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations, the generating the initial population of solutions of portfolio allocations including systematically generating the initial population of solutions to substantially cover the space defined by the competing objectives and the plurality of constraints; and generating an efficient frontier in the space based on the initial population, the efficient frontier for use in investment decisioning.
A process is described for evaluating the decision-making confidence of a process and system for at least a partial underwriting of insurance policies where placement of an insurance application to an underwriting category is based on its similarity to previous insurance applications. The confidence factor computed is a measure of the correctness of the decision for a given application for insurance.
A method and system for creating healthy operating envelope from only data samples obtained during normal operation/behavior of dynamic systems is provided. This method determines healthy operating envelope by clustering a stream of discrete event code sequences from the underlying system under normal operation condition only. The method is unsupervised, that is, requiring no prior knowledge of event code patterns corresponding to different operation conditions. Such created envelope can be used for fault detection and health monitoring of dynamic systems.
Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions.
A robust process for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.
-2012: Fuzzy Systems Pioneer, IEEE Computational
Intelligence Society (CIS)
-2008 II Cajastur Int'l Prize for Soft Computing (ECSC)
-2005: Meritorious Service Award, IEEE CIS
-2005: Fellow, Int'l Fuzzy Systems Association (IFSA)
-2004: Fellow, Institute of Electrical and Electronics
-1996: Fellow, Association for the Advancement of Artificial
-1993: Coolidge Fellowship Award, GE Global Research
-1986: King-Sun Fu Award, North American Fuzzy
Information Processing Soc. (NAFIPS)
RECENT PROFESSIONAL SOCIETY ACTIVITIES
-2010-2011: Chair, IEEE CIS Fellow Committee
-2008-10: Vice-Chair (Americas), IEEE CIS Technical
Committee on Emergent Technologies
-2007-2009: Member, IEEE Fellow Committee
-2005-2012: Vice-President Finance, IEEE CIS
-2005-2006: Chair, IEEE Frank Rosenblatt Award
-2004-2005: Chair, IEEE CIS Fellow Committee
-2003: Past President, IEEE CIS
-2002: President, IEEE Neural Network Society (IEEE-NNS)
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