Ivan Jordanov

Ivan Jordanov

Dr

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Biography

I am a Reader in Computational Intelligence at the School of Computing. I have a PhD in Computer Aided Optimization, MSc in Applied Mathematics and Informatics from the Technical University of Sofia; and BSc in Mechanical and Electrical Engineering from the Naval University, Bulgaria.

I joined the University of Portsmouth in 2003 after 3 years with De Montfort University as a Senior Lecturer; 2 years with the University of Wales Institute, Cardiff as a Senior Researcher; and 16 years with the Technical University Sofia as Associate Professor.

Research Interests

Computational Intelligence (Machine Learning, Deep Learning, Neural Networks, Data Analytics, and Heuristic Global Optimisation)

My current research interest includes all aspects of analysis, design, modelling and investigation of supervised and unsupervised Machine Learning paradigms. These comprise designing, developing and proposing classical and deep learning neural network architectures (Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) neural networks), applied for solving real world image and pattern recognition, identification, and classification problems, based on large and big data sets. For example, I am PI (and grant holder) of a current 3-year (2021-2024) EPSRC EP-V002511-1 project “Deep Learning Models for Fetal Monitoring and Decision Support in Labour”, a collaboration with Oxford University and Oxford NHS Trust (total cost: £762k (£610k funded by EPSRC)). Other projects include: UK Space Agency, UNITAR and UNOSAT “Common Sensing: Work Package 350: Climate-related hazards”; Expedia Inc. “Distributed Neural Networks for Missing Big Data Imputation”; SBRI “Deep Learning for Firearm Detection from X-ray Images”; DSTL/CDE “Neural Networks Generic Radar Signals Identification and Classification”, EPSRC (GR/S01702/01) "Investigation of Intelligent Techniques for Interpreting Freeform Surfaces from On-line Sketching"; NATO Science and Cooperation “Intelligent Techniques in Computer Graphics and Visualisation”; UNIDO “Optimization Techniques in Computer Graphics and CAD”; ROYAL SOCIETY Research Fellowship; RAE Distinguished Visiting Fellowship (as a host), and others.

My research in Data Analytics area is related to statistical pre-processing, dimensionality reduction and discrimination (PCA, ICA, LDA, etc.), dealing with missingness and data imputation methods, imbalanced datasets, data augmentation, etc.

My earlier research in Global Optimization field includes investigation of evolutionary techniques and heuristic approaches based on so-called Low Discrepancy Sequences, applied for combinatorial and global search problems.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 13 - Climate Action
  • SDG 15 - Life on Land

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  • Deep learning for volatility forecasting in asset management

    Petrozziello, A., Troiano, L., Serra, A., Jordanov, I., Storti, G., Tagliaferri, R. & La Rocca, M., 15 Jul 2022, (Early online) In: Soft Computing. 22 p.

    Research output: Contribution to journalArticlepeer-review

    Open Access
    File
    7 Downloads (Pure)
  • LULC image classification with convolutional neural network

    Balarabe, A. T. & Jordanov, I., 12 Oct 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Institute of Electrical and Electronics Engineers, p. 5985-5988 4 p. (IEEE International Geoscience and Remote Sensing Symposium proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Automated deep learning for threat detection in luggage from x-ray images

    Petrozziello, A. & Jordanov, I., 14 Nov 2019, SEA 2019: Analysis of Experimental Algorithms. Kotsireas, I., Pardalos, P., Parsopoulos, K. E., Souravlias, D. & Tsokas, A. (eds.). Springer, p. 505-512 5 p. (Lecture Notes in Computer Science; vol. 11544).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
    258 Downloads (Pure)
  • Convergence properties of quantum evolutionary algorithms on high dimension problems

    Wright, J. & Jordanov, I., 31 Jan 2019, In: Neurocomputing. 326–327, p. 82-99 18 p.

    Research output: Contribution to journalArticlepeer-review

    Open Access
    File
    255 Downloads (Pure)
  • Feature based multivariate data imputation

    Petrozziello, A. & Jordanov, I., Mar 2019, Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers. Nicosia, G., Giuffrida, G., Nicosia, G., Pardalos, P., Sciacca, V. & Umeton, R. (eds.). Springer Verlag, p. 26-37 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11331 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
    259 Downloads (Pure)