Dr Hassan Eshkiki

Tutor in Computer Science, Computer Science

h.g.eshkiki@swansea.ac.uk

Admin - 015
Ground Floor
Computational Foundry
Bay Campus

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0001-7795-453X

ResearchGate

About

Dr Hassan Eshkiki is an engaged computer science researcher with a focus on machine learning, particularly in the areas of computer vision and medical image analysis. To expand his research areas, he mostly employs various datasets and metrics. He has experience in genetics and bioinformatics.

Apart from his research, he has a strong interest in teaching and makes a meaningful contribution to programs that promote inclusivity and enhance the student experience. He is constantly searching for scholarships and financing programs that encourage creative projects aimed at creating an environment that is conducive to study.

Areas Of Expertise

Machine learning
Explainable AI
Efficient Neural network
Computer vision
Serious game

Career Highlights

Teaching Interests

Artificial intelligent
Software engineering
Algorithms

Supervision:

Mapping cellular interactivity in networks of human cardiac data- a machine learning approach
PhD
Other supervisor: Prof Christopher Georg, Dr. Fabio Caraffini

Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (current)
PhD
Other supervisor: Dr Fabio Caraffini

Collaborations

Publications

Research Highlights

Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 841-845). IEEE.
https://doi.org/10.1109/ictai52525.2021.00134, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58534
Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
https://doi.org/10.1109/tnnls.2022.3176197, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60038
Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
http://dx.doi.org/10.1007/978-3-031-67285-9_8
Enobun, A., Anakwenze, U., Taherkhani, A., Anastassi, Z., Caraffini, F., & Eshkiki, H. (2024). Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net. Lecture Notes in Computer Science (pp. 149-159). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-67285-9_11, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67384
http://dx.doi.org/10.1007/978-3-031-67285-9_11

All Research

Journal Articles

Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
https://doi.org/10.1109/tnnls.2022.3176197, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60038

Book Chapters

Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
http://dx.doi.org/10.1007/978-3-031-67285-9_8
Enobun, A., Anakwenze, U., Taherkhani, A., Anastassi, Z., Caraffini, F., & Eshkiki, H. (2024). Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net. Lecture Notes in Computer Science (pp. 149-159). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-67285-9_11, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67384
http://dx.doi.org/10.1007/978-3-031-67285-9_11

Conference Contributions

Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 841-845). IEEE.
https://doi.org/10.1109/ictai52525.2021.00134, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58534

Supervision

Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (current)

PhD

Other supervisor: Dr Fabio Caraffini