Scholar
Ioannis Patras
Google Scholar ID: OBYLxRkAAAAJ
Professor, Queen Mary, University of London
Citations & Impact
Citations
9,455
H-index
39
i10-index
91
Publications
20
Co-authors
37
list available
Publications
Resume (English only)
Academic Achievements
- Over 300 publications in the most selective Journals and Conferences in the area of Computer Vision and Machine Learning, including CVPR, ICCV, ECCV, NeurIPS, EMNLP, TPAMI, IJCV, TIP.
- - Area chair of all major conferences in the area of Computer Vision and associate editor in three journals (Image and Vision Computing; Pattern Recognition; and Computer Vision and Image Understanding)
- - Research funded by the EPSRC, EU FP7, and direct bilateral collaborations with research institutes and the industry
- - Member of the college of the EPSRC, and regular reviewer and evaluator for funding agencies in EU, UK, Canada, France, Netherlands, Belgium, and others
- - Senior member of the IEEE and Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS)
Research Experience
- Professor in Computer Vision and Machine Learning at Queen Mary University of London
- - Head of the Centre for Multimodal AI
- - Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS)
Background
- Research interests: Multimodal AI, using Machine Learning, Computer Vision, and Signal Processing methodologies to learn from multiple sources concepts that enable Intelligent Systems to understand the world and to communicate and collaborate with humans. Recent works and research interests are at the cross-roads of Computer Vision and NLP, and currently evolve around three themes:
- - Learning to recognize behavior, emotions, and cognitive states of people by analyzing their images, video, and neuro-physiological signals
- - Learning across modalities, and in particular at the intersections of language and vision, using large, pretrained language and audio-visual models
- - Learning from generative models and learning to control generation for privacy, interpretability, and control purposes.
