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Pedro Simões Coelho

Pedro Simões Coelho image

Pedro Simões Coelho

Pedro Simões Coelho is presently Full Professor and President of the Scientific Council of the NOVA Information Management School (NOVA IMS) of Universidade Nova de Lisboa. He is also researcher in the Information Management Research Center (MagIC) of NOVA University. He is presently senior expert near the European Commission for the area of statistical methods and sampling techniques.

He is member of the board of the European Master of Official Statistics (EMOS), member of the Ischools accreditation committee, member of the Portuguese Health Technologies Commission and head of information and statistics in NOVA Clinical Research Unit (NOVA CRU). He is former President of the General Assembly of the Portuguese Association for Classification and Data Analysis (CLAD), former member of the Portuguese High Council for Statistics (CSE) and President of the Fiscal Board of the European Center of Statistics for the Developing Countries (CESD-Lisboa).

Pedro S. Coelho has “aggregation” (post-doctoral degree) in Statistics (NOVA University), PhD in Statistics (NOVA University), MSc in Statistics and Information Management and a graduation in Systems Engineering. He has been lecturer of near 30 graduate and undergraduate courses, including Structural Equation Modeling, Research Methodologies, Sampling, Survey Methodology, Marketing Research, Data Collection Methodologies and Data Analysis.

He is author of about 200 studies and projects, resulting in more than 500 research reports. He is also author of more than 100 refereed publications. He has presented near 100 seminars, invited conferences and oral presentations in scientific conferences.

Pedro S. Coelho has been consultant and trainer for several organizations, namely for the European Commission, Eurostat, the Portuguese Statistical Office, the Portuguese Central Bank and several National Statistical Offices around the world. For these institutions has been developing methodologies and offering training in data analysis techniques.