CURRICULUM VITAE
Nicola Orlando
Born in Taranto, Italy, 23-05-1986
Lives & works in Geneva, Switzerland
Languages: Italian (native), English (fluent), French (beginner)
Education
2011 - 2014 Ph.D. in Physics. Salento University (Lecce, Italy). Graduated with maximum evaluation (Excellent) | Ph.D. thesis https://cds.cern.ch/record/1755832.
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2008 - 2010 M.Sc. in Physics. Salento University (Lecce, Italy). Graduated with 110/110 and honours.
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2005 - 2008 B.Sc. in Physics. Salento University (Lecce, Italy). Graduated with 110/110 and honours.
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Positions
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2018 - present Research Scientist. ​Barcelona Institute of Science and Technology, Institut de Fisica d'Altes Energies (Barcelona, Spain).
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2015 - 2018 Research Staff. The University of Hong Kong (Hong Kong, SAR China).
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2014 - 2015 Postdoctoral Researcher. Aristotle University of Thessaloniki (Thessaloniki, Greece).
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2014 Short term fellow. University College London (London, United Kingdom).
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2012 - 2013 Research Fellow. European Organisation for Nuclear Research (Geneva, Switzerland).
Skills and research experience
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Development and maintenance of multiple software projects for data mining, data visualisation, and statistical analysis.
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Engineering of machine learning classification algorithms based on decision trees and feed-forward neural networks. Development of related software for high-level feature engineering and data visualisation. Application of decision trees and feed-forward neural networks to big-data research problems, mainly classification.
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Statistical data analysis (development of likelihood models, hypothesis testing) for big-data research problems.
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Engineering of a naïve Bayesian classifiers based on use of predictive models, application to big-data research problems.
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Development of algorithms for data deconvolution and application to big-data research problems.
Development and software engineering of algorithms for parametric or non-parametric linear regression and
application to big-data research problems.
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Developments of advanced analytical or numerical predictive models.