The advanced automative analytics factory - A data scientist's perspective

Daneil Neagu

The advanced automative analytics factory - A data scientist's perspective
Daneil Neagu
University of Bradford, UK
Short Bio:
Daniel Neagu is Professor of Computing with Faculty of Engineering and Informatics of the University of Bradford, UK, which he joined in 2002. He previously served as Associate Professor at the Department of Computer Science & Engineering, University of Galati, Romania (1993-2001) and Post-Doctoral Research Fellow with Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy (2001-2002). His research focuses on Machine Learning techniques applied in Engineering, Product Safety and Toxicology, Healthcare, Sustainable Smart Cities with a focus on Data Quality, Big Data and Model Governance and Analysis. The main theme throughout his academic work is to develop models of multidisciplinary information systems by the fusion of experts knowledge and digital information. He strongly believes Computer Science is now a media for knowledge representation, exchange and retrieval to deliver and solve multidisciplinary objectives and purposes. Daniel leads Bradford's Artificial Intelligence Research Group (AIRe). His research is funded by EC FPs and ERDF, RC-UK, I-UK, industry and government bodies. He contributes as external examiner, expert and auditor for international research organisations, universities, EC H2020, FET and IMI panels, RC-UK, BCS and IEEE specialist groups. Daniel Neagu served as General or Technical Chair for a number of relevant international conferences and workshops (such as UKCI2014, BCS SGAI AI-2011, -2012 and -2013, UKCI2014, DSS-2018, EDMA-2017 and 2018), and is Associated Editor for Wiley's Expert Systems journal and invited editor for Springer Neural Computing and Applications journal special issue on Predictive Analytics Using Machine Learning in 2014/15, and Springer Soft Computing journal special issue on Computational Intelligence in 2015. Daniel teaches Data Mining, Big Data Systems and Analysis and Software Development. He delivered modules, presentations and invited training sessions to BBC Academy, NATO training courses, industry, EC FPs consortia schools, and coordinated data hackathons and research seminars series.
Big data is currently the new fuel empowering industry 4.0 towards an effective digital economy. Current developments in data gathering using sensors and data-driven technologies connected through the Internet of Things enable automotive industry among others to join the efforts to process data into information with the aim to extract knowledge and generate intelligent actions. For example the automotive industry invests currently in big data systems from the car lifecycle, including product development and vehicles in the field to R&D. Consequently the demand for effective big data processes increases, with challenges derived from both scarcity of effective tools and expertise to issues regarding data quality and model management. This talk reviews the journey of the Advanced Automotive Analytics (AAA) team at the University of Bradford, with details and examples of recent and current contributions. The team's vision of the Automotive Analytics Factory as the emerging model for an integration of data, models and tools applied to the vehicle lifecycle is introduced with references to AAA research projects, publications and events.
Tuesday, May 15th, 2018 - 15.00
DIBRIS, Valletta Puggia, Conference Room


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