Staff profile

Dr Stamos Katsigiannis is an Assistant Professor in Computer Science at the Department of Computer Science of Durham University. His research interests lie in the fields of bioinformatics, health informatics, affective computing, image analysis, machine learning, image and video quality, and GPU computing.

Before joining Durham, he was a Postdoctoral Research Fellow and a Lecturer at the School of Computing, Engineering and Physical Sciences of the University of the West of Scotland, UK (2016-2020), as well as a junior researcher/PhD candidate at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens, Greece (2009-2016).

He holds a BSc (Hons.) degree in Informatics and Telecommunications from the National and Kapodistrian University of Athens, Greece, an MSc in Computer Science from the Athens University of Economics and Business, Greece, and a PhD degree in Computer Science from the National and Kapodistrian University of Athens, Greece.

Google Scholar Profile

• Applied machine learning
• Bioinformatics
• Bio-signal processing
• Health informatics
• Affective computing
• Image/video processing
• Image/video quality
• GPU computing

Chapter in book

• Al-Qahtani, M., Katsigiannis, S., & Ramzan, N. (2020). Information Retrieval from Electronic Health Records. In M. A. Imran, R. Ghannam, & Q. H. Abbasi (Eds.), Engineering and technology for healthcare (117-128). Wiley-IEEE Press
• Katsigiannis, S., Rabah, H., & Ramzan, N. (2020). A machine learning driven solution to the problem of perceptual video quality metrics. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET
• Althobaiti, T., Katsigiannis, S., West, D., Rabah, H., & Ramzan, N. (2020). Machine learning-based affect detection within the context of human-horse interaction. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET
• Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2020). Artificial Intelligence for Affective Computing: An emotion recognition case study. In M. Z. Shakir, & N. Ramzan (Eds.), AI for emerging verticals; human-robot computing, sensing and networking. IET
• Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2020). EEG-based biometrics: Effects of template ageing. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET
• Katsigiannis, S., Ahmad, W., & Ramzan, N. (2019). 5G: Disruption in Media and Entertainment. In Enabling 5G Communication Systems to Support Vertical Industries (179-190). Wiley-IEEE Press. https://doi.org/10.1002/9781119515579.ch8
• Katsigiannis, S., Keramidas, E., & Maroulis, D. (2013). FLBP: Fuzzy Local Binary Patterns. In Local Binary Patterns: New Variants and Applications (149-175). Springer Verlag. https://doi.org/10.1007/978-3-642-39289-4_7
• Katsigiannis, S., Papaioannou, G., & Maroulis, D. (2013). A Real-Time Video Encoding Scheme Based on the Contourlet Transform. In Design and Architectures for Digital Signal Processing. https://doi.org/10.5772/51735

Conference Paper

• Jayasinghe, J., Katsigiannis, S., & Malasinghe, L. (2024). Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography. In 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET). https://doi.org/10.1109/ICECET58911.2023.10389182
• Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., & Arevalillo-Herráez, M. (2023). Towards Automatic Tutoring of Custom Student-Stated Math Word Problems. In AIED 2023: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (639-644). https://doi.org/10.1007/978-3-031-36336-8_99
• Li, R., Katsigiannis, S., & Shum, H. P. (2022). Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding. In 2022 IEEE International Conference on Image Processing (ICIP) Proceedings (2346-2350). https://doi.org/10.1109/icip46576.2022.9897644
• Elsafoury, F., Wilson, S. R., Katsigiannis, S., & Ramzan, N. (2022). SOS: Systematic Offensive Stereotyping Bias in Word Embeddings.
• Wu, Y., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2022). On the benefits of using Hidden Markov Models to predict emotions. . https://doi.org/10.1145/3503252.3531323
• Gascoigne-Burns, J., & Katsigiannis, S. (2022). A Localisation Study of Deep Learning Models for Chest X-ray Image Classification. . https://doi.org/10.1109/bhi56158.2022.9926904
• Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022). Multi-modal lung ultrasound image classification by fusing image-based features and probe information. . https://doi.org/10.1109/bibe55377.2022.00018
• Elsafoury, F., Katsigiannis, S., Wilson, S., & Ramzan, N. (2021). Does BERT pay attention to cyberbullying?. . https://doi.org/10.1145/3404835.3463029
• Katsigiannis, S., Arnau-González, P., Arevalillo-Herráez, M., & Ramzan, N. (2021). Single-channel EEG-based subject identification using visual stimuli. . https://doi.org/10.1109/bhi50953.2021.9508581
• Blakey, W. A., Katsigiannis, S., Hajimirza, N., & Ramzan, N. (2020). Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking. In IS&T International Symposium on Electronic Imaging 2020 : Human vision and electronic imaging (129.1-129.9). https://doi.org/10.2352/issn.2470-1173.2020.11.hvei-129
• Arevalillo-Herráez, M., Chicote-Huete, G., Ferri, F., Ayesh, A., Boticario, J., Katsigiannis, S., …Arnau-González, P. (2019). On using EEG signals for emotion modeling and biometrics.
• Kostopoulou, E., Katsigiannis, S., & Maroulis, D. (2019). SpotDSQ: A 2D-Gel Image Analysis Tool for Protein Spot Detection, Segmentation and Quantification. . https://doi.org/10.1109/bibe.2019.00015
• Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2019). On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems. . https://doi.org/10.1109/bibe.2019.00077
• Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2019). ECG-based affective computing for difficulty level prediction in Intelligent Tutoring Systems. . https://doi.org/10.1109/ucet.2019.8881872
• Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2019). Image-Evoked Affect and its Impact on EEG-Based Biometrics. . https://doi.org/10.1109/icip.2019.8803315
• Alzubi, R., Ramzan, N., Alzoubi, H., & Katsigiannis, S. (2018). SNPs-based Hypertension Disease Detection via Machine Learning Techniques. . https://doi.org/10.23919/iconac.2018.8748972
• Alzahrani, I. R., Ramzan, N., Katsigiannis, S., & Amira, A. (2018). Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement. . https://doi.org/10.1007/978-3-319-78753-4
• Althobaiti, T., Katsigiannis, S., West, D., Bronte-Stewart, M., & Ramzan, N. (2018). Affect Detection for Human-Horse Interaction. . https://doi.org/10.1109/ncg.2018.8593113
• Malasinghe, L., Katsigiannis, S., Ramzan, N., & Dahal, K. (2018). Remote Heart Rate Extraction Using Microsoft KinectTM v2.0. . https://doi.org/10.1145/3232059.3232060
• Arnau-González, P., Althobaiti, T., Katsigiannis, S., & Ramzan, N. (2017). Perceptual video quality evaluation by means of physiological signals. . https://doi.org/10.1109/qomex.2017.7965651
• Kostopoulou, E., Katsigiannis, S., Maroulis, D., Pappa, K., & Anagnou, N. (2015). Spot detection in 2D-gel electrophoresis images.
• Katsigiannis, S., Dimitsas, V., & Maroulis, D. (2015). A GPU vs CPU performance evaluation of an experimental video compression algorithm. . https://doi.org/10.1109/qomex.2015.7148134
• Kostopoulou, E., Katsigiannis, S., & Maroulis, D. (2015). A custom grow-cut based scheme for 2D-gel image segmentation. . https://doi.org/10.1109/embc.2015.7318879
• Kostopoulou, E., Katsigiannis, S., Maroulis, D., Pappa, K., & Anagnou, N. (2015). A novel approach for accurate 2D-gel image segmentation.
• Katsigiannis, S., Maroulis, D., & Papaioannou, G. (2013). A GPU based real-time video compression method for video conferencing. . https://doi.org/10.1109/icdsp.2013.6622719
• Katsigiannis, S., Zacharia, E., & Maroulis, D. (2013). Enhancing the performance of a microarray gridding algorithm via GPU computing techniques. . https://doi.org/10.1109/bibe.2013.6701689
• Katsigiannis, S., & Maroulis, D. (2013). Parallel computing techniques for performance enhancement of a cDNA microarray gridding algorithm. . https://doi.org/10.1109/isspit.2013.6781922
• Katsigiannis, S., Papaioannou, G., & Maroulis, D. (2012). A contourlet transform based algorithm for real-time video encoding. . https://doi.org/10.1117/12.924327
• Katsigiannis, S. (2010). Contourlet Transform and Support Vector Machines for Image Analysis and Processing.
• Katsigiannis, S., Keramidas, E. G., & Maroulis, D. (2010). Contourlet Transform for Texture Representation of Ultrasound Thyroid Images. In Artificial Intelligence Applications and Innovations. https://doi.org/10.1007/978-3-642-16239-8_20

Journal Article

• Arevalillo-Herráez, M., Katsigiannis, S., Alqahtani, F., & Arnau-González, P. (2023). Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems. Knowledge-Based Systems, 280, Article 111052. https://doi.org/10.1016/j.knosys.2023.111052
• Katsigiannis, S., Seyedzadeh, S., Agapiou, A., & Ramzan, N. (2023). Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning. Journal of Building Engineering, 76, Article 107105. https://doi.org/10.1016/j.jobe.2023.107105
• Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., Althobaiti, T., & Arevalillo-Herráez, M. (2023). Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems. IEEE Access, 11, 67030-67039. https://doi.org/10.1109/access.2023.3290478
• Alzubi, R., Alzoubi, H., Katsigiannis, S., West, D., & Ramzan, N. (2022). Automated Detection of Substance-Use Status and Related Information from Clinical Text. Sensors, 22(24), Article 9609. https://doi.org/10.3390/s22249609
• Malasinghe, L., Katsigiannis, S., Dahal, K., & Ramzan, N. (2022). A Comparative Study of Common Steps in Video-based Remote Heart Rate Detection Methods. Expert Systems with Applications, 207, Article 117867. https://doi.org/10.1016/j.eswa.2022.117867
• Arevalillo-Herráez, M., Segura-García, J., Arnau-González, P., & Katsigiannis, S. (2022). Wrap reduction algorithm for Fringe Projection Profilometry. Optics and Lasers in Engineering, 158, Article 107185. https://doi.org/10.1016/j.optlaseng.2022.107185
• Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022). IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification. Computer Methods and Programs in Biomedicine, 226, Article 107141. https://doi.org/10.1016/j.cmpb.2022.107141
• Okolo, G. I., Katsigiannis, S., Althobaiti, T., & Ramzan, N. (2021). On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays. Sensors, 21(17), Article 5702. https://doi.org/10.3390/s21175702
• Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2021). BED: A new dataset for EEG-based biometrics. IEEE Internet of Things Journal, 8(15), 12219-12230. https://doi.org/10.1109/jiot.2021.3061727
• Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2021). Using wearable physiological sensors for affect-aware Intelligent Tutoring Systems. IEEE Sensors Journal, 21(3), 3366-3378. https://doi.org/10.1109/jsen.2020.3023886
• Arnau-González, P., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2021). On the influence of affect in EEG-based subject identification. IEEE Transactions on Affective Computing, 12(2), 391-401. https://doi.org/10.1109/taffc.2018.2877986
• Elsafoury, F., Katsigiannis, S., Pervez, Z., & Ramzan, N. (2021). When the timeline meets the pipeline: A survey on automated cyberbullying detection. IEEE Access, 9, 103541-103563. https://doi.org/10.1109/access.2021.3098979
• Althobaiti, T., Katsigiannis, S., & Ramzan, N. (2020). Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning. Sensors, 20(13), Article 3777. https://doi.org/10.3390/s20133777
• Kerdjidj, O., Amira, A., Ghanem, K., Ramzan, N., Katsigiannis, S., & Chouireb, F. (2019). An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform. Microprocessors and Microsystems, 67, 131-139. https://doi.org/10.1016/j.micpro.2019.03.007
• Althobaiti, T., Katsigiannis, S., West, D., & Ramzan, N. (2019). Examining Human-Horse Interaction by Means of Affect Recognition via Physiological Signals. IEEE Access, 7, 77857-77867. https://doi.org/10.1109/access.2019.2922037
• Katsigiannis, S., Willis, R., & Ramzan, N. (2019). A QoE and Simulator Sickness Evaluation of a Smart-Exercise-Bike Virtual Reality System via User Feedback and Physiological Signals. IEEE Transactions on Consumer Electronics, 65(1), 119-127. https://doi.org/10.1109/tce.2018.2879065
• Katsigiannis, S., Scovell, J., Ramzan, N., Janowski, L., Corriveau, P., Saad, M. A., & Van Wallendael, G. (2018). Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality. Quality and User Experience, 3(1), Article 6. https://doi.org/10.1007/s41233-018-0019-8
• Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98-107. https://doi.org/10.1109/jbhi.2017.2688239
• Katsigiannis, S., Zacharia, E., & Maroulis, D. (2017). MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU. IEEE Journal of Biomedical and Health Informatics, 21(3), 867-874. https://doi.org/10.1109/jbhi.2016.2537922
• Kostopoulou, E., Katsigiannis, S., & Maroulis, D. (2015). 2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information. Computer Methods and Programs in Biomedicine, 122(1), 26-39. https://doi.org/10.1016/j.cmpb.2015.06.007
• Katsigiannis, S., Zacharia, E., & Maroulis, D. (2015). Grow-Cut Based Automatic cDNA Microarray Image Segmentation. IEEE Transactions on NanoBioscience, 14(1), 138-145. https://doi.org/10.1109/tnb.2014.2369961
• Katsigiannis, S., Keramidas, E., & Maroulis, D. (2010). A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classification

Other (Digital/Visual Media)

• Katsigiannis, S., Papaioannou, G., & Maroulis, D. (2015). CVC: The Contourlet Video Compression algorithm for real-time applications. [[Media unknown]]

Report

• Owens, R., Copilah-Ali, J., Kolomeets, M., Malviya, S., Markeviciute, K., Olabode, S., …Farrand, B. (2023). Reimagining AI Governance: a Response by AGENCY to the UK Government's White Paper AI Regulation. SSRN: AGENCY project