School of Computing

Staff

Mr Nedyalko Petrov

  • Qualifications: First Class BSc (Hons) in Computer Science, PGCertHE
  • Role Title: Part-time Lecturer and Research Student
  • Address: Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK
  • Telephone: +44 (0)23 9284 6463
  • Email: nedyalko.petrov@port.ac.uk
  • Department: School of Computing
  • Faculty: Faculty of Technology

Biography

I joined the University in 2010 as a bursary-funded PhD student with a research topic on “Intelligent Methods for Pattern Recognition and Optimisation”. During the course of my studies I have been involved in several projects within the areas of Intelligent Visual Inspection Systems, Radar Emitter Signals Recognition and Classification (under a project with a defence contractor), Intelligent Optimisation of Industrial Processes (in particular, optimisation of a Hydrotreating process for British Petroleum (BP) under an EPSRC-funded KTN (Knowledge Transfer Network) placement) and Intelligent Aerodynamic Surface Optimisation (under a sKTN (short Knowledge Transfer Partnership) placement with AIRBUS).

Since my second semester at the University, I have been involved in delivering practicals and seminars for several units (listed below), as well as one to one coaching at the Tutor Support Centre of the School of Computing.

I have been appointed as a part-time lecturer at the School of Computing since 2012.

Teaching Responsibilities

Since I joined the University, I have been involved in the following teaching activities:

  • Introduction to Web Programming (lectures and practicals)
  • Introduction to Programming (pracitals and seminars)
  • Advanced Programming Concepts (practicals)
  • Neural Networks and Genetic Algorithms (practicals)

Research

Current PhD research

  • “Intelligent Methods for Pattern Recognition and Optimisation”, under the supervision of Dr. Ivan Jordanov, Dr. Bim Briggs and Prof. Max Bramer

Other research interests

  • Computational Intelligence
  • Machine Vision and Image Processing
  • Machine Learning (Supervised and Unsupervised)
  • Mathematical Modelling and Optimisation
  • Pattern Recognition and Classification
  • Evolutionary Computation
  • Algorithmic Trading