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  1. Home
  2. …
  3. People
  4. Academics
  5. Dr Alex (Aram) Ter-Sarkisov
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photo of Alex (Aram) Ter-Sarkisov

Dr Alex (Aram) Ter-Sarkisov

Lecturer in Artificial Intelligence

School of Science & Technology Department of Computer Science

Contact details

  • +44 (0)20 7040 8346
  • alex.ter-sarkisov@city.ac.uk

Address

Dr Alex (Aram) Ter-Sarkisov A309M, College Building [A]
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom

Personal links

  • Google Scholar
  • GitHub
  • About
  • Publications

About

Overview

Alex Ter-Sarkisov is a member of CitAI research group at City University.

His recent work is on the segmentation of instances of lesions and lungs, and classification of chest CT scans of COVID-19 patients using deep learning models like Mask R-CNN and Faster R-CNN. He also works on Generative Adversarial Networks and Neural Style Transfer for the logos of heavy metal bands.

His academic background (PhD thesis) includes mathematical analysis of the runtime of Evolutionary Algorithms.

Qualifications

  • PhD, Massey University, New Zealand, 2012

Employment

  • Postdoctoral Researcher, Dublin Institute of Technology (Republic of Ireland), 2015 – 2018
  • Postdoctoral Researcher, Université du Maine (France), 2014 – 2015
  • Postdoctoral Researcher, University of Waterloo (Canada), 2012 – 2013

Publications

Featured publications

  1. Ter-Sarkisov, A. and Alonso, E. (2022). Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks. doi:10.1007/978-3-030-95531-1_30
  2. Ter-Sarkisov, A., Kelleher, J.D., Earley, B., Keane, M. and Ross, R.J. (2018). Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network. British Machine Vision Conference 2018 3-7 September, Newcastle upon Tyne, UK.
  3. Ter-Sarkisov, A. (2022). COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features. Applied Intelligence, 52(9), pp. 9664–9675. doi:10.1007/s10489-021-02731-6.

    [publisher’s website]

  4. Ter-Sarkisov, A. (2022). One Shot Model for COVID-19 Classification and Lesions Segmentation in Chest CT Scans Using Long Short-Term Memory Network With Attention Mechanism. IEEE Intelligent Systems, 37(3), pp. 54–64. doi:10.1109/mis.2021.3135474.

    [publisher’s website]

  5. Ter-Sarkisov, A. (2022). One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features. Applied Soft Computing, 116, pp. 108261–108261. doi:10.1016/j.asoc.2021.108261.

    [publisher’s website]

  6. Ter-Sarkisov, A. (2021). Lightweight Model for the Prediction of COVID-19 Through the Detection and Segmentation of Lesions in Chest CT Scans. International Journal of Automation, Artificial Intelligence and Machine Learning, 2(1), pp. 1–15.
  7. Ter-Sarkisov, A. (2020). Detection and segmentation of lesion areas in chest CT scans for the prediction of COVID-19. Science in Information Technology Letters, 1(2), pp. 92–99. doi:10.31763/sitech.v1i2.202.

    [publisher’s website]

Publications by category

Conference papers and proceedings (9)

  • Hafeez, A.B., Alonso, E. and Ter-Sarkisov, A. (2021). Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 13-16 December. doi:10.1109/icmla52953.2021.00167
  • Ananda, , Karabag, C., Ter-Sarkisov, A., Alonso, E. and Reyes-Aldasoro, C.C. (2020). Radiography Classification: A Comparison between Eleven Convolutional Neural Networks. 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA) 19-22 October. doi:10.1109/mcna50957.2020.9264285
  • Ter-Sarkisov, A. (2020). Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos. ICPRAM 18 Feb 2020 – 21 Mar 2020, Malta.
  • Ter-Sarkisov, A., Ross, R. and Kelleher, J. (2017). Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis. 2017 14th Conference on Computer and Robot Vision (CRV) 16-19 May, Edmonton, Canada. doi:10.1109/crv.2017.25
  • Ter-Sarkisov, A., Schwenk, H., Bougares, F. and Barrault, L. (2015). Incremental Adaptation Strategies for Neural Network Language Models. 3rd Workshop on Continuous Vector Space Models and their Compositionality 26-31 July, Beijing, China. doi:10.18653/v1/w15-4006
  • Ter-Sarkisov, A. (2012). Elitism levels traverse mechanism for the derivation of upper bounds on unimodal functions. 2012 IEEE Congress on Evolutionary Computation 10-15 June, Brisbane, Australia. doi:10.1109/CEC.2012.6256619
  • Ter-Sarkisov, A. and Marsland, S. (2011). Convergence of a recombination-based elitist evolutionary algorithm on the royal roads test function. 24th Australasian Joint Conference on Artificial Intelligence, AI 2011 5-8 December, Perth, Australia. doi:10.1007/978-3-642-25832-9_37
  • Ter-Sarkisov, A. and Marsland, S. (2011). Convergence properties of two (μ+λ) Evolutionary Algorithms on onemax and royal roads test functions. 3rd International Joint Conference on Computational Intelligence 24-26 October, Paris, France.
  • Ter-Sarkisov, A., Marsland, S. and Holland, B. (2010). The K-bit-swap: A new genetic algorithm operator. 12th Annual conference on Genetic and Evolutionary Computation 7-11 July, Portland, Oregon. doi:10.1145/1830483.1830626

Journal articles (2)

  • Ananda, A., Ngan, K.H., Karabağ, C., Ter-Sarkisov, A., Alonso, E. and Reyes-Aldasoro, C.C. (2021). Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures. Sensors, 21(16), pp. 5381–5381. doi:10.3390/s21165381.

    [publisher’s website]

  • Ter-Sarkisov, A. and Marsland, S. (2017). K-Bit-Swap: a new operator for real-coded evolutionary algorithms. Soft Computing, 21(20), pp. 6133–6142. doi:10.1007/s00500-016-2170-6.

    [publisher’s website]

Thesis/dissertation

  • Ter-Sarkisov, A. Computational complexity of elitist population-based evolutionary algorithms : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand. (PhD Thesis)

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