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Dr Alexia Zoumpoulaki
(she/her)
Academic and research staff
School of Computer Science and Informatics
- Available for postgraduate supervision
Overview
I am a Lecturer (assistant professor) in Computer Science and Informatics at Cardiff University, UK, specialising in AI, machine learning for Human Factors Applications. In the past I have worked in various interdisciplinary research projects, including memory classification, deception detection and crowd simulations. A lot of my work has focused in developing methods for analysing and classifying time series data.
Currently I am interested on multimodal processing of videos. Facial (micro) expressions - emotion recognition, text analysis, voice analysis in the area of deception detection using machine learning. I am interested in incorporating features extracted from these modalities with other psychological factors as well as features from eyetracking.
Broadly I am interested in congitive (computational) (neuro) science and how computer science can help model behaviour and cognitive processes. This understanding will help us build exciting new applications.
Publication
2021
- Finnegan, D., Zoumpoulaki, A. and Eslambolchilar, P. 2021. Does mixed reality have a Cassandra Complex?. Frontiers in Virtual Reality 2, article number: 673547. (10.3389/frvir.2021.673547)
2020
- Bowman, H., Brooks, J. L., Hajilou, O., Zoumpoulaki, A. and Litvak, V. 2020. Breaking the circularity in circul aranalyses: simulations and formal treatment of the flattened average approach. PLoS Computational Biology 16(11), article number: e1008286. (10.1371/journal.pcbi.1008286)
2019
- Alsufyani, A. et al. 2019. Breakthrough percepts of famous faces. Psychophysiology 56(1), article number: e13279. (10.1111/psyp.13279)
2018
- Belal, S. et al. 2018. Identification of memory reactivation during sleep by EEG classification. NeuroImage 176, pp. 203-214. (10.1016/j.neuroimage.2018.04.029)
2017
- Brooks, J. L., Zoumpoulaki, A. and Bowman, H. 2017. Data-driven region-of-interest selection without inflating Type I error rate. Psychophysiology 54(1), pp. 100-113. (10.1111/psyp.12682)
2016
- Zoumpoulaki, A., Gootjes-Dreesbach, L., Bergstrom, Z., Alsufyani, A. and Bowman, H. 2016. Context matters: Driving perceptual breakthrough through contextual priming. Journal of Vision 16(12), article number: 1037. (10.1167/16.12.1037)
2015
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2015. Latency as a region contrast: Measuring ERP latency differences with Dynamic Time Warping. Psychophysiology 52(12), pp. 1559-1576. (10.1111/psyp.12521)
- Zoumpoulaki, A., Alsufyani, A. and Bowman, H. 2015. Corrigendum to resampling the peak, some dos and don'ts. Psychophysiology 52(5), pp. 726-726. (10.1111/psyp.12444)
- Zoumpoulaki, A., Alsufyani, A. and Bowman, H. 2015. Resampling the peak, some dos and don'ts. Psychophysiology 52(3), pp. 444-448. (10.1111/psyp.12363)
2013
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2013. ERP latency contrasts using Dynamic Time Warping algorithm. BMC Neuroscience 14(Supp 1), pp. P434. (10.1186/1471-2202-14-S1-P434)
- Alsufyani, A., Zoumpoulaki, A., Filetti, M. and Bowman, H. 2013. A new method for detecting deception in Event Related Potentials using individual-specific weight templates [Abstract]. BMC Neuroscience 14(S1), pp. P34. (10.1186/1471-2202-14-S1-P34)
2010
- Zoumpoulaki, A., Avradinis, N. and Vosinakis, S. 2010. A multi-agent simulation framework for emergency evacuations incorporating personality and emotions. Presented at: 6th Hellenic Conference on Artificial Intelligence (SETN 2010), Athens, Greece, 4-7 May 2010 Presented at Konstantopoulos, S. et al. eds.Advances in Artificial Intelligence: Theories, Models, and Applications, Vol. 6040. Lecture Notes in Artificial Intelligence Springer pp. 423-428., (10.1007/978-3-642-12842-4_54)
Articles
- Finnegan, D., Zoumpoulaki, A. and Eslambolchilar, P. 2021. Does mixed reality have a Cassandra Complex?. Frontiers in Virtual Reality 2, article number: 673547. (10.3389/frvir.2021.673547)
- Bowman, H., Brooks, J. L., Hajilou, O., Zoumpoulaki, A. and Litvak, V. 2020. Breaking the circularity in circul aranalyses: simulations and formal treatment of the flattened average approach. PLoS Computational Biology 16(11), article number: e1008286. (10.1371/journal.pcbi.1008286)
- Alsufyani, A. et al. 2019. Breakthrough percepts of famous faces. Psychophysiology 56(1), article number: e13279. (10.1111/psyp.13279)
- Belal, S. et al. 2018. Identification of memory reactivation during sleep by EEG classification. NeuroImage 176, pp. 203-214. (10.1016/j.neuroimage.2018.04.029)
- Brooks, J. L., Zoumpoulaki, A. and Bowman, H. 2017. Data-driven region-of-interest selection without inflating Type I error rate. Psychophysiology 54(1), pp. 100-113. (10.1111/psyp.12682)
- Zoumpoulaki, A., Gootjes-Dreesbach, L., Bergstrom, Z., Alsufyani, A. and Bowman, H. 2016. Context matters: Driving perceptual breakthrough through contextual priming. Journal of Vision 16(12), article number: 1037. (10.1167/16.12.1037)
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2015. Latency as a region contrast: Measuring ERP latency differences with Dynamic Time Warping. Psychophysiology 52(12), pp. 1559-1576. (10.1111/psyp.12521)
- Zoumpoulaki, A., Alsufyani, A. and Bowman, H. 2015. Corrigendum to resampling the peak, some dos and don'ts. Psychophysiology 52(5), pp. 726-726. (10.1111/psyp.12444)
- Zoumpoulaki, A., Alsufyani, A. and Bowman, H. 2015. Resampling the peak, some dos and don'ts. Psychophysiology 52(3), pp. 444-448. (10.1111/psyp.12363)
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2013. ERP latency contrasts using Dynamic Time Warping algorithm. BMC Neuroscience 14(Supp 1), pp. P434. (10.1186/1471-2202-14-S1-P434)
- Alsufyani, A., Zoumpoulaki, A., Filetti, M. and Bowman, H. 2013. A new method for detecting deception in Event Related Potentials using individual-specific weight templates [Abstract]. BMC Neuroscience 14(S1), pp. P34. (10.1186/1471-2202-14-S1-P34)
Conferences
- Zoumpoulaki, A., Avradinis, N. and Vosinakis, S. 2010. A multi-agent simulation framework for emergency evacuations incorporating personality and emotions. Presented at: 6th Hellenic Conference on Artificial Intelligence (SETN 2010), Athens, Greece, 4-7 May 2010 Presented at Konstantopoulos, S. et al. eds.Advances in Artificial Intelligence: Theories, Models, and Applications, Vol. 6040. Lecture Notes in Artificial Intelligence Springer pp. 423-428., (10.1007/978-3-642-12842-4_54)
- Brooks, J. L., Zoumpoulaki, A. and Bowman, H. 2017. Data-driven region-of-interest selection without inflating Type I error rate. Psychophysiology 54(1), pp. 100-113. (10.1111/psyp.12682)
- Zoumpoulaki, A., Gootjes-Dreesbach, L., Bergstrom, Z., Alsufyani, A. and Bowman, H. 2016. Context matters: Driving perceptual breakthrough through contextual priming. Journal of Vision 16(12), article number: 1037. (10.1167/16.12.1037)
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2015. Latency as a region contrast: Measuring ERP latency differences with Dynamic Time Warping. Psychophysiology 52(12), pp. 1559-1576. (10.1111/psyp.12521)
- Zoumpoulaki, A., Alsufyani, A. and Bowman, H. 2015. Resampling the peak, some dos and don'ts. Psychophysiology 52(3), pp. 444-448. (10.1111/psyp.12363)
- Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M. and Bowman, H. 2013. ERP latency contrasts using Dynamic Time Warping algorithm. BMC Neuroscience 14(Supp 1), pp. P434. (10.1186/1471-2202-14-S1-P434)
- Alsufyani, A., Zoumpoulaki, A., Filetti, M. and Bowman, H. 2013. A new method for detecting deception in Event Related Potentials using individual-specific weight templates [Abstract]. BMC Neuroscience 14(S1), pp. P34. (10.1186/1471-2202-14-S1-P34)
- Zoumpoulaki, A., Avradinis, N. and Vosinakis, S. 2010. A multi-agent simulation framework for emergency evacuations incorporating personality and emotions. Presented at: 6th Hellenic Conference on Artificial Intelligence (SETN 2010), Athens, Greece, 4-7 May 2010 Presented at Konstantopoulos, S. et al. eds.Advances in Artificial Intelligence: Theories, Models, and Applications, Vol. 6040. Lecture Notes in Artificial Intelligence Springer pp. 423-428., (10.1007/978-3-642-12842-4_54)
Research
Research interests
I am interested in applying AI, machine learning techniques to build human factors appliactions, focusing on areas suchs as attention, multitasking and decision making. I am using physiological measurements such as gaze, pupil dilation, eeg in combination with video, text and voice analysis to study the above areas.
I am interested in behaviours under stressful situations, and how to develop technologies to help humans with decision making and task perfomance.
I have expertise in analysing and classifying neuroimaging/time series data.
Teaching
Computational Thinking CM6114/CM6614: The course aims to enable students to translate real world problems into computer code. The focus is on developing the ability to apply core concepts by writing code that solves basic problems, laying the foundation for productive coding in later modules. On successful completion of this module, it is expected that students are able to approach problems in a computational way through logical thinking, reformulation, abstraction, decomposition and appropriate data representation. Students will also develop a basic understanding of the most popular problem solving algorithms (search, sort, routing, resource allocation), describe them in a scientific way and evaluate their complexity. Finally, they will cover how information is stored and accessed in a computer and they will be able to translate across different number systems.
Undergraduate and Postrgraduate project supervision: Past and current projects include:
- High Frequency Oscillation Detection Using Wavelet Analysis and Convolutional Neural Networks
- Deception Detection in Text with Tranfer Learning
- An Application for Comparing and Visualising M/EEG Pre-processing Pipelines
- Detecting Lies using Eyetracker and video analysis
- An Explorative Study into the Effects of Visualisation On Deception Detection Accuracy
- Multimodal deception detection in Videos with Deep Learning
Biography
- PhD in Computer Science (Computational Neuroscience) - University of Kent, Canterbury, UK
- MSc in Advanced Computer Science (Computational Intelligence): Distinction - University of Kent, Canterbury, UK
- MSc Product & Systems Design Engineering: Distinction - University of the Aegean, Syros, Greece
- BEng Information & Communication Systems Engineering - University of the Aegean, Samos, Greece
Supervisions
I am currently looking to support PhD applications in the area of automatic deception detection.
I am also interested in supervising PhD students in the areas of:
- Video analysis - automatic micro expressions, gestures detection
- Text analysis - deception detection using machine learning/neural networks
- Voice analysis - features for deception detection
- Eyetrackers - feature extraction for classification
- HCI - visual attention, multitasking and problem solving - using eyetracking and/or neuroimaging
