Jon Ander Gomez
Associate Professor, Universitat Politècnica de València/DeepHealth project
Dr. Jon Ander Gómez Adrián is Associate Professor of computer science at UPV and is member of the Pattern Recognition and Human Language Technology (PRHLT) research center. His research interests include pattern recognition, machine/deep learning and their application to automatic speech recognition, handwritten text recognition, predictive maintenance in smart industry and computer vision. Currently, he is Vice-dean for Lifelong Learning of the School of Informatics, Director of the Master in Big Data Analytics of the UPV, and the UPV representative in the Big Data Value Association (BDVA). He is coauthor of articles in journals and international conferences. He has served in program committees of some journals and conferences. Jon Ander Gómez is also co-founder and CDO of Solver Machine Learning, a Spin-off of the UPV. Dr. Gómez has participated in several contracts with companies and public administration. He has transferred different technologies around fraud detection, anomaly detection and predictive maintenance in industry, among others. Recent works are (1) Machine Learning advisor in a CDTI project to develop ML models for predictive maintenance in the Ford Engine Plant in Almussafes (Valencia, Spain) 2015; (2) Energy consumption prediction for a Spanish energy trader, 2016; (3) Fraud detection in credit/debit card transactions in collaboration with BBVA Data & Analytics, 2017; (4) Early warning system to detect bad practices in public procurement, Generalitat Valenciana, 2017-2018; (5) Detection of illegal activities in banking operations with BANKIA, 2018-2019; and (6) Control and prediction of drinking water quality with EMIVASA, 2019-2020. Currently he is the Technical Manager of the DeepHealth H2020 European project with Grant Agreement 825111.
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This track will bring together leading initiatives that are paving the way for what could become functional implementations of European-governed data sharing spaces. For Europe to compete at an international level, its smaller but highly diversified data producers need to join forces. The European Data Strategy outlines a roadmap to enable and realise data sharing spaces as a means to power up AI in Europe by increasing access to data. The longterm vision is for an initial number of sector-based data sharing spaces to interoperate. The need to comply with European values, ethics and legislation when handling data should not be considered restrictive, but as a safeguard for risk-free data sharing that encourage increased participation. In this track, we want to move beyond the ‘What’ and consider ‘How’ all this can be achieved through convergence and standardization efforts by projects, organisations and other entities possessing the right technical know-how, experiences and commitment.
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