Staff profile

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

He is also a Fellow of the Higher Education Academy (HEA) since 2017.

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

• Information Retrieval from Electronic Health Records
  
  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. (pp. 117-128). Wiley-IEEE Press.
• A machine learning driven solution to the problem of perceptual video quality metrics
  
  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.
• EEG-based biometrics: Effects of template ageing
  
  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.
• Artificial Intelligence for Affective Computing: An emotion recognition case study
  
  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.
• Machine learning-based affect detection within the context of human-horse interaction
  
  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.
• 5G: Disruption in Media and Entertainment
  
  Katsigiannis, S., Ahmad, W., & Ramzan, N. (2019). 5G: Disruption in Media and Entertainment. In Enabling 5G Communication Systems to Support Vertical Industries (pp. 179-190). Wiley-IEEE Press. https://doi.org/10.1002/9781119515579.ch8
• FLBP: Fuzzy Local Binary Patterns
  
  Katsigiannis, S., Keramidas, E., & Maroulis, D. (2013). FLBP: Fuzzy Local Binary Patterns. In Local Binary Patterns: New Variants and Applications (pp. 149-175). Springer Verlag. https://doi.org/10.1007/978-3-642-39289-4_7
• A Real-Time Video Encoding Scheme Based on the Contourlet Transform
  
  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

• SKDU at De-Factify 4.0: Vision Transformer with Data Augmentation for AI-Generated Image Detection
  
  Malviya, S., Bhowmik, N., & Katsigiannis, S. (in press). SKDU at De-Factify 4.0: Vision Transformer with Data Augmentation for AI-Generated Image Detection. Presented at De-factify 4.0 Workshop at the 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA.
• Human Intracranial EEG Biometric Identification
  
  Belay, B., & Katsigiannis, S. (in press). Human Intracranial EEG Biometric Identification. Presented at International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Copenhagen, Denmark.
• SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection
  
  Maviya, S., Arnau-González, P., Arevalillo-Herráez, M., & Katsigiannis, S. (in press). SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection. Presented at De-factify 4.0 Workshop at 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA.
• Evidence Retrieval for Fact Verification using Multi-stage Reranking
  
  Malviya, S., & Katsigiannis, S. (2024). Evidence Retrieval for Fact Verification using Multi-stage Reranking. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 7295-7308). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.428
• SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task
  
  Malviya, S., & Katsigiannis, S. (2024). SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER) (pp. 99-107). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.fever-1.11
• Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography
  
  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). IEEE. https://doi.org/10.1109/ICECET58911.2023.10389182
• Towards Automatic Tutoring of Custom Student-Stated Math Word Problems
  
  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 (pp. 639-644). Springer. https://doi.org/10.1007/978-3-031-36336-8_99
• Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding
  
  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 (pp. 2346-2350). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icip46576.2022.9897644
• SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
  
  Elsafoury, F., Wilson, S. R., Katsigiannis, S., & Ramzan, N. (2022, October). SOS: Systematic Offensive Stereotyping Bias in Word Embeddings. Presented at 29th International Conference on Computational Linguistics (COLING 2022), Gyeongju, Republic of Korea.
• On the benefits of using Hidden Markov Models to predict emotions
  
  Wu, Y., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2022, July). On the benefits of using Hidden Markov Models to predict emotions. Presented at ACM Conference on User Modeling, Adaptation and Personalization (UMAP), Barcelona. https://doi.org/10.1145/3503252.3531323
• Multi-modal lung ultrasound image classification by fusing image-based features and probe information
  
  Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022). Multi-modal lung ultrasound image classification by fusing image-based features and probe information. Presented at IEEE International Conference on BioInformatics and BioEngineering (BIBE 2022), Taichung, Taiwan. https://doi.org/10.1109/bibe55377.2022.00018
• A Localisation Study of Deep Learning Models for Chest X-ray Image Classification
  
  Gascoigne-Burns, J., & Katsigiannis, S. (2022). A Localisation Study of Deep Learning Models for Chest X-ray Image Classification. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926904
• Does BERT pay attention to cyberbullying?
  
  Elsafoury, F., Katsigiannis, S., Wilson, S., & Ramzan, N. (2021, July). Does BERT pay attention to cyberbullying?. Presented at 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online. https://doi.org/10.1145/3404835.3463029
• Single-channel EEG-based subject identification using visual stimuli
  
  Katsigiannis, S., Arnau-González, P., Arevalillo-Herráez, M., & Ramzan, N. (2021). Single-channel EEG-based subject identification using visual stimuli. Presented at 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Online. https://doi.org/10.1109/bhi50953.2021.9508581
• Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking
  
  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. (pp. 129.1-129.9). Society for Imaging Science and Technology. https://doi.org/10.2352/issn.2470-1173.2020.11.hvei-129
• On using EEG signals for emotion modeling and biometrics
  
  Arevalillo-Herráez, M., Chicote-Huete, G., Ferri, F., Ayesh, A., Boticario, J., Katsigiannis, S., Ramzan, N., & Arnau-González, P. (2019, October). On using EEG signals for emotion modeling and biometrics. Presented at 33rd European Simulation and Modelling Conference (ESM), Palma de Mallorca, Spain.
• ECG-based affective computing for difficulty level prediction in Intelligent Tutoring Systems
  
  Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2019). ECG-based affective computing for difficulty level prediction in Intelligent Tutoring Systems. Presented at 4th International Conference on UK/China Emerging Technologies (UCET), Glasgow, United Kingdom. https://doi.org/10.1109/ucet.2019.8881872
• SpotDSQ: A 2D-Gel Image Analysis Tool for Protein Spot Detection, Segmentation and Quantification
  
  Kostopoulou, E., Katsigiannis, S., & Maroulis, D. (2019). SpotDSQ: A 2D-Gel Image Analysis Tool for Protein Spot Detection, Segmentation and Quantification. Presented at 19th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Athens, Greece. https://doi.org/10.1109/bibe.2019.00015
• Image-Evoked Affect and its Impact on EEG-Based Biometrics
  
  Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2019). Image-Evoked Affect and its Impact on EEG-Based Biometrics. Presented at IEEE International Conference on Image Processing (IEEE ICIP), Taipei, Taiwan. https://doi.org/10.1109/icip.2019.8803315
• On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems
  
  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. Presented at 19th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Athens, Greece. https://doi.org/10.1109/bibe.2019.00077
• Remote Heart Rate Extraction Using Microsoft KinectTM v2.0
  
  Malasinghe, L., Katsigiannis, S., Ramzan, N., & Dahal, K. (2018). Remote Heart Rate Extraction Using Microsoft KinectTM v2.0. Presented at 10th International Conference on Bioinformatics and Biomedical Technology (ICBBT), Amsterdam, Netherlands. https://doi.org/10.1145/3232059.3232060
• SNPs-based Hypertension Disease Detection via Machine Learning Techniques
  
  Alzubi, R., Ramzan, N., Alzoubi, H., & Katsigiannis, S. (2018). SNPs-based Hypertension Disease Detection via Machine Learning Techniques. Presented at 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, United Kingdom. https://doi.org/10.23919/iconac.2018.8748972
• Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement
  
  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. Presented at 5th International Symposium on Data Mining Applications (SDMA), Riyadh, Saudi Arabia. https://doi.org/10.1007/978-3-319-78753-4
• Affect Detection for Human-Horse Interaction
  
  Althobaiti, T., Katsigiannis, S., West, D., Bronte-Stewart, M., & Ramzan, N. (2018). Affect Detection for Human-Horse Interaction. Presented at 21st Saudi Computer Society National Computer Conference (NCC), Riyadh, Saudi Arabia. https://doi.org/10.1109/ncg.2018.8593113
• ES1D: A Deep Network for EEG-Based Subject Identification
  
  Arnau-González, P., Katsigiannis, S., Ramzan, N., Tolson, D., & Arevalillo-Herráez, M. (2017). ES1D: A Deep Network for EEG-Based Subject Identification. In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 81-85). IEEE. https://doi.org/10.1109/BIBE.2017.00-74
• Perceptual video quality evaluation by means of physiological signals
  
  Arnau-González, P., Althobaiti, T., Katsigiannis, S., & Ramzan, N. (2017). Perceptual video quality evaluation by means of physiological signals. Presented at 9th International Conference on Quality of Multimedia Experience (QoMEX), Erfurt, Germany. https://doi.org/10.1109/qomex.2017.7965651
• Spot detection in 2D-gel electrophoresis images
  
  Kostopoulou, E., Katsigiannis, S., Maroulis, D., Pappa, K., & Anagnou, N. (2015, May). Spot detection in 2D-gel electrophoresis images. Presented at 6th Panhellenic Conference on Biomedical Technology (ΕΛΕΒΙΤ), Athens, Greece.
• A novel approach for accurate 2D-gel image segmentation
  
  Kostopoulou, E., Katsigiannis, S., Maroulis, D., Pappa, K., & Anagnou, N. (2015). A novel approach for accurate 2D-gel image segmentation. Presented at 10th Conference of the Hellenic Society for Computational Biology & Bioinformatics, Athens, Greece.
• A custom grow-cut based scheme for 2D-gel image segmentation
  
  Kostopoulou, E., Katsigiannis, S., & Maroulis, D. (2015). A custom grow-cut based scheme for 2D-gel image segmentation. Presented at 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Milan, Italy. https://doi.org/10.1109/embc.2015.7318879
• A GPU vs CPU performance evaluation of an experimental video compression algorithm
  
  Katsigiannis, S., Dimitsas, V., & Maroulis, D. (2015). A GPU vs CPU performance evaluation of an experimental video compression algorithm. Presented at 7th International Workshop on Quality of Multimedia Experience (QoMEX), Pylos, Greece. https://doi.org/10.1109/qomex.2015.7148134
• Parallel computing techniques for performance enhancement of a cDNA microarray gridding algorithm
  
  Katsigiannis, S., & Maroulis, D. (2013). Parallel computing techniques for performance enhancement of a cDNA microarray gridding algorithm. Presented at IEEE International Symposium on Signal Processing and Information Technology (IEEE ISSPIT), Athens, Greece. https://doi.org/10.1109/isspit.2013.6781922
• A GPU based real-time video compression method for video conferencing
  
  Katsigiannis, S., Maroulis, D., & Papaioannou, G. (2013). A GPU based real-time video compression method for video conferencing. Presented at 18th International Conference on Digital Signal Processing (IEEE DSP), Santorini, Greece. https://doi.org/10.1109/icdsp.2013.6622719
• Enhancing the performance of a microarray gridding algorithm via GPU computing techniques
  
  Katsigiannis, S., Zacharia, E., & Maroulis, D. (2013). Enhancing the performance of a microarray gridding algorithm via GPU computing techniques. Presented at 13th IEEE International Conference on BioInformatics and BioEngineering (IEEE BIBE), Chania, Greece. https://doi.org/10.1109/bibe.2013.6701689
• A contourlet transform based algorithm for real-time video encoding
  
  Katsigiannis, S., Papaioannou, G., & Maroulis, D. (2012). A contourlet transform based algorithm for real-time video encoding. Presented at SPIE Photonics Europe, Real-Time Image and Video Processing 2012, Brussels, Belgium. https://doi.org/10.1117/12.924327
• Contourlet Transform and Support Vector Machines for Image Analysis and Processing
  
  Katsigiannis, S. (2010, October). Contourlet Transform and Support Vector Machines for Image Analysis and Processing. Presented at 1st International Conference for undergraduate and graduate students in Informatics and Related Applications (EUREKA! 2010), Patras, Greece.
• Contourlet Transform for Texture Representation of Ultrasound Thyroid Images
  
  Katsigiannis, S., Keramidas, E. G., & Maroulis, D. (2010). Contourlet Transform for Texture Representation of Ultrasound Thyroid Images. In Artificial Intelligence Applications and Innovations. Springer Verlag. https://doi.org/10.1007/978-3-642-16239-8_20

Journal Article

• CLN: A multi-task deep neural network for chest X-ray image localisation and classification
  
  Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2025). CLN: A multi-task deep neural network for chest X-ray image localisation and classification. Expert Systems With Applications, 288, Article 128162. https://doi.org/10.1016/j.eswa.2025.128162
• A study on the impact of different components of a traditional webcam-based 2D gaze tracking algorithm
  
  Blakey, W., Katsigiannis, S., & Ramzan, N. (2025). A study on the impact of different components of a traditional webcam-based 2D gaze tracking algorithm. IEEE Sensors Journal, 25(12), 22151-22164. https://doi.org/10.1109/JSEN.2025.3564397
• BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
  
  Li, R., Katsigiannis, S., Kim, T.-K., & Shum, H. P. H. (2025). BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction. IEEE Transactions on Neural Networks and Learning Systems. Advance online publication. https://doi.org/10.1109/TNNLS.2025.3545268
• Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
  
  Li, R., Qiao, T., Katsigiannis, S., Zhu, Z., & Shum, H. P. (2025). Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction. IEEE Transactions on Circuits and Systems for Video Technology. Advance online publication. https://doi.org/10.1109/TCSVT.2025.3539522
• Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems
  
  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
• Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning
  
  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
• Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems
  
  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
• Automated Detection of Substance-Use Status and Related Information from Clinical Text
  
  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
• A Comparative Study of Common Steps in Video-based Remote Heart Rate Detection Methods
  
  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
• Wrap reduction algorithm for Fringe Projection Profilometry
  
  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
• IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification
  
  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
• On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
  
  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
• BED: A new dataset for EEG-based biometrics
  
  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
• Using wearable physiological sensors for affect-aware Intelligent Tutoring Systems
  
  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
• When the timeline meets the pipeline: A survey on automated cyberbullying detection
  
  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
• On the influence of affect in EEG-based subject identification
  
  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
• Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning
  
  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
• Examining Human-Horse Interaction by Means of Affect Recognition via Physiological Signals
  
  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
• A QoE and Simulator Sickness Evaluation of a Smart-Exercise-Bike Virtual Reality System via User Feedback and Physiological Signals
  
  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
• An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform
  
  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
• Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality
  
  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
• DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices
  
  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
• MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU
  
  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
• 2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information
  
  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
• Grow-Cut Based Automatic cDNA Microarray Image Segmentation
  
  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
• A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classification
  
  Katsigiannis, S., Keramidas, E., & Maroulis, D. (2010). A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classification. Engineering Intelligent Systems, 18(3/4).

Other (Digital/Visual Media)

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

Report

• AGENCY—written evidence (FON0017) for House of Lords Communications and Digital Select Committee inquiry: The future of news: impartiality, trust and technology
  
  Owens, R., Nijia Zhang, V., Malviya, S., Kalameyets, M., Durrant, A., Elliot, K., Farrand, B., Katsigiannis, S., Neesham, C., & Shi, L. (2024). AGENCY—written evidence (FON0017) for House of Lords Communications and Digital Select Committee inquiry: The future of news: impartiality, trust and technology. UK Parliament.
• Reimagining AI Governance: a Response by AGENCY to the UK Government's White Paper AI Regulation
  
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• AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models
  
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