I joined Aston University as a PhD student in January 2012. Before that, I obtained my first PhD degree, in Systems Engineering, from the Department of Automatic Control and Applied Informatics, at the “Gheorghe Asachi” Technical University of Iasi, Romania. I got my BSc in Applied Science at the same university, in 2008.
I spent 6 semesters (during and after the completion of my first PhD) combining research with teaching – I have been a laboratory demonstrator at my Alma Mater in fields such as real time programming, software projects management and systems theory.
During my last three years as an undergraduate student, I worked as a junior software developer with Fastpath Logic Inc., a small company specialised in providing support for parsing and translating hardware descriptive languages. This experience has added a practical side to my professional background, one that I found most useful in providing my research with immediate applicability in solving realistic problems.
Qualifications & Education
BSc with First Class Honours in Applied Sciences, “Gheorghe Asachi” Technical University, Iasi, Romania, 2008
PhD in Systems Engineering, “Gheorghe Asachi” Technical University, Iasi, Romania, 2011
October 2008 – December 2011: TA in Real Time Programming, Software Projects Management and System Theory, Department of Automatic Control and Applied Informatics, “Gheorghe Asachi” Technical University, Iasi, Romania.
March 2006 – October 2008: Junior Software Developer, Fastpath Logic Inc, Iasi, Romania.
Ontology based autonomic systems, evolutionary computation, descriptive logic frameworks
Patelli A. and L. Ferariu (2010). Elite Based Multiobjective Genetic Programming in Nonlinear Systems Identification. Advances in Electrical and Computer Engineering, vol 10(1), pp. 94-99, ISSN 1582-7445, e-ISSN 1844-7600
Patelli A. and L. Ferariu (2011). A Regressive Schema Theory based Tool for GP Evolved Nonlinear Models. Proceedings of the 17th International Conference on Automation and Computing, Huddersfield, UK, pp. 215-220, ISBN 978-1-86218-098-7.
Patelli A. and L. Ferariu (2010). Elitist Multiobjective Nonlinear Systems Identification with Insular Evolution and Diversity Preservation. Proc. of 2010 IEEE World Congress on Computational Intelligence - Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 2076-2081, ISBN 978-1-4244-6910-9, IEEE catalog no. CFP10ICE-DVD.
Patelli A. and L. Ferariu (2010). Increasing Crossover Operator Efficiency in Multiobjective Nonlinear Systems Identification. Proc. of 2010 IEEE International Conference on Intelligence Systems, London, UK, pp. 426-431, ISBN 978-1-4244-5164-7, IEEE catalog no. CFP10802-CDR.
Patelli A. and L. Ferariu (2010). Genetic Programming Based Tools for Nonlinear Systems Identification. Doctoral Consortium of 2010 IEEE International Conference on Networking, Sensing and Control, Chicago, IL, USA, http://www.ele.uri.edu/faculty/he/icnscdc/DoctoralProgarm-Final.pdf.
Patelli A. and L. Ferariu (2009). Nonlinear Systems Identifications by Means of Genetic Programming. Proc. of European Control Conference, Budapest, Hungary, pp. 502-507, ISBN 978-963-311-369-1.
Ferariu L. and A. Patelli (2009). Migration-Based Multiobjective Genetic Programming for Nonlinear System Identification. Proc. of SACI 2009 5th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 475-480, ISBN 978-1-4244-4478-6, IEEE catalog no. CFP0945C-CDR.
Ferariu L. and A.Patelli (2012). Genetic Programming for System Identification. Formal and Practical Aspects of Autonomic Computing and Networking: Specification, Development and Verification, ed. P. Cong-Vinh, pp. 135-168, IGI Global, doi:10.4018/978-1-60960-845-3.
Patelli A. and L. Ferariu (2011). Regressor Survival Rate Estimation for Enhanced Crossover Configuration. Adaptive and Natural Computing Algorithms, Lecture Notes in Computer Science, vol. 6593, pp. 290-300, Springer Heidelberg, ISSN 0302-0743.
Ferariu L. and A. Patelli (2009). Multiobjective Genetic Programming for Nonlinear System Identification. Adaptive and Natural Computing Algorithms, Lecture Notes on Computer Science, vol. 5495, pp. 233-242, Springer Berlin/Heidleberg, ISSN 0302-9743 (print) 1611-3349 (online).