Prof. Panagiotis Liatsis
Dr. Panos Liatsis is a Professor and the Interim Chair of the Department of Computer Science at Khalifa University in Abu Dhabi. Prior to joining the Khalifa University, he was Professor of Image Processing and Head of Department of Electrical and Electronic Engineering at City University London.
His main research interests are in the areas of image processing, pattern recognition and machine learning. Over his academic career, he worked on developing tools and systems for medical image analysis and diagnosis, industrial process inspection and condition monitoring, and decision support with applications to a number of areas including autonomous vehicles, factory automation, tomography and power systems.
Panos has published over 200 articles in high quality international journals and refereed conference proceedings. He also delivered several invited presentations in international conferences and is the editor of three international conference proceedings. Furthermore, he is in the editorial boards of international journals and a member of the international program committee of international conferences in the fields of image and signal processing.
He is a member of the Peer Review College of the Engineering and Physical Sciences Research Council and regularly acts as a project reviewer and assessor on behalf of the Research Executive Agency, the Research Promotion Foundation in Cyprus and the Hong Kong Research Grants Council. He is a European Engineer (Eur Ing), a senior member of the Institute of Electrical and Electronic Engineers and a member of the Technical Chamber of Greece.
Advisor to current students:
- Zainab Husain
- Nadya Abdel Madjid
- Mahmoud Mustafa Khalil
- PhD – Electrical Engineering and Electronics, University of Manchester Institute of Science and Technology, Manchester, UK, 2002.
- MSc – Electrical Engineering, University of Thrace, Xanthi, Greece. 1990.
- ECCE732 Machine Learning and Applications
- ECCE456 Image Processing and Analysis
- Three-dimensional vision: reconstruction using co-evolutionary Genetic Algorithms and constraint non-linear programming techniques, 3D robotic vision systems based on lidar/laser.
- Sensor fusion: Kalman filtering for fusion of camera and GPS data, wavelet transform for integration of air-coupled ultrasound and ground penetrating radar, committees of local expert neural networks for multi-dimensional fusion, multi-modal biometrics fusion using 1-class classification algorithms.
- Neural networks: polynomial neural networks for object recognition and time series forecasting, deep learning in image deblurring.
- Electrical Impedance Tomography: wavelet transform modelling of the forward and inverse problems for multiscale reconstruction, algorithms for anisotropic regularization, pre-conditioning schemes.
- Medical imaging: active contour models for blood vessel segmentation in CT images, geometric modelling using dynamic programming, blood flow estimation and non-invasive estimation of Fractional Flow Reserve, enhancement of retinal images and estimation of transit times in fluorescein angiography.
- R. Al-Shabandar, A.J. Hussain, P. Liatsis, R. Keight, ‘Analysing learners behaviour in MOOCs: an examination of performance and motivation using a data-driven approach’, IEEE Access, https://doi.org/10.1109/ACCESS.2018.2876755, 2018.
- E.O. Rodrigues, A. Conci, and P. Liatsis, ‘Morphological Classifiers’, Pattern Recognition, https://doi.org/10.1016/j.patcog.2018.06.010, Vol. 84, pp. 82-96, 2018.
- A.J. Hussain, P. Liatsis, M. Khalaf, H. Tawfik and H. Al-Asker, ‘A dynamic neural network architecture with immunology inspired optimization for weather data forecasting’, Big Data Research, https://doi.org/10.1016/j.bdr.2018.04.002, Vol. 14, pp. 8192, 2018.
- E. O. Rodrigues, P. Liatsis, L. Satoru Ochi and A. Conci, ‘Fractal Triangular Search: A metaheuristic for image content search’, IET Image Processing, https://doi.org/10.1049/iet-ipr.2017.0790, Vol. 12, No. 8, pp. 1475-1484, 2018.
- S.M.R.S. Beheshti, P. Liatsis and M. Rajarajan, ‘A CAPTCHA model based on visual psychophysics: using the brain to distinguish between human users and automated computer bots”, Computers & Security, https://doi.org/10.1016/j.cose.2017.08.006, Vol. 70, pp. 596-617, 2017.
- E.O. Rodrigues, L.O. Rodrigues, L.S.N. Oliveira, A. Conci and P. Liatsis, ‘Automated recognition of the pericardium contour on processed CT images using genetic algorithms’, Computers in Biology and Medicine, Vol. 87, pp. 38-45, 2017.
- E.O. Rodrigues, V.H.A. Pinheiro, P. Liatsis and A. Conci, ‘Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes’, Computers in Biology and Medicine, Vol. 89, pp. 520-529, , 2017.
- M.M. Jawaid, R. Rajani, P. Liatsis, C.C. Reyes-Aldasoro and G.G. Slabaugh, ‘A hybrid energy model for region based curve evolution- application to CTA coronary segmentation’, Computer Methods and Programs in Biomedicine, Vol. 144, pp. 189-202, 2017.
- E.O. Rodrigues, L. Torok, P. Liatsis, J. Viterbo and A. Conci, ‘K-MS: A novel clustering algorithm based on morphological reconstruction’, Pattern Recognition, Vol. 26, pp. 392-403, 2017.
- Y. Wu, T. Mu, P. Liatsis and J.Y. Goulermas, ‘Computation of heterogeneous object co-embeddings from relational measurements’, Pattern Recognition, Vol. 65, pp. 146-163, 2017.
- A. Zifan and P. Liatsis, “Patient-specific computational models of coronary arteries using monoplane X-ray angiograms”, Computational and Mathematical Methods in Medicine, Vol. 2016, http://dx.doi.org/10.1155/2016/2695962, 2016.
- A. Lazareva, P. Liatsis and F.G. Rauscher, “Hessian-Log filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images”, JOSA-A, Vol. 33, No.1, pp. 84-94, 2016.
- Q.D. Tran and P. Liatsis, “RABOC: An approach to handle class imbalance in multimodal biometric authentication”, Neurocomputing, Vol. 188, pp. 167-177, 2016.