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Dr Mihalis Nicolaou

Staff details

PositionLecturer
Department Computing
Email m.nicolaou (@gold.ac.uk)
Dr Mihalis Nicolaou

Mihalis A. Nicolaou is lecturer in Computer Science. Previously, Mihalis was a postdoctoral research associate at Imperial College London (Department of Computing), where he also completed his PhD.

For more information, see Mihalis Nicolaou's website

Research Interests

Mihalis' research interests span the areas of machine learning and computer vision, particularly motivated by problems arising in the audio-visual analysis of affective behaviour under real-world conditions.  

Publications

Book Section

Machine Learning Methods for Social Signal Processing
Rudovic, O.; Nicolaou, Mihalis and Pavlovic, V.. 2014. Machine Learning Methods for Social Signal Processing. In: , ed. Social Signal Processing. Cambridge University Press.

Article

Deep Canonical Time Warping for simultaneous alignment and representation learning of sequences
Trigeorgis, G.; Nicolaou, M. A.; Schuller, B. and Zafeiriou, S.. 2017. Deep Canonical Time Warping for simultaneous alignment and representation learning of sequences. IEEE transactions on Pattern Analysis and Machine Intelligence,

Facial Affect ``in-the-wild": A survey and a new database
Zafeiriou, S.; Papaioannou, A.; Kotsia, I.; Nicolaou, M. A. and Zhao, G.. 2016. Facial Affect ``in-the-wild": A survey and a new database. Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition Workshops (CVPRW '16), Affect in the wild workshop.,

Adieu Features? End-to- End Speech Emotion Recognition using a Deep Convolutional Recurrent Network
Trigeorgis, George; Ringeval, Fabien; Brueckner, Raymond; Marchi, Erik; Nicolaou, Mihalis; Schuller, Björn and Zafeiriou, Stefanos. 2016. Adieu Features? End-to- End Speech Emotion Recognition using a Deep Convolutional Recurrent Network. Proceedings of IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP), 2016,

Robust Correlated and Individual Component Analysis
Panagakis, Yannis; Nicolaou, Mihalis; Zafeiriou, Stefanos and Pantic, Maja. 2016. Robust Correlated and Individual Component Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence,

Probabilistic Slow Features for Behavior Analysis
Zafeiriou, Lazaros; Nicolaou, Mihalis; Zafeiriou, Stefanos; Nikitidis, Symeon and Pantic, Maja. 2015. Probabilistic Slow Features for Behavior Analysis. IEEE Transactions on Neural Networks and Learning Systems, PP(99), p. 1.

A Unified Framework for Probabilistic Component Analysis
Nicolaou, Mihalis; Zafeiriou, Stefanos and Pantic, Maja. 2014. A Unified Framework for Probabilistic Component Analysis. Proceedings of the European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD’14), pp. 469-484.

Robust Canonical Correlation Analysis: Audio-visual Fusion for Learning Continuous Interest
Nicolaou, Mihalis; Panagakis, Yannis; Zafeiriou, Stefanos and Pantic, Maja. 2014. Robust Canonical Correlation Analysis: Audio-visual Fusion for Learning Continuous Interest. Proceedings of IEEE Int’l Conf. Acoustics, Speech and Signal Processing (ICASSP 2014),

Dynamic Probabilistic CCA for Analysis of Affective Behaviour and Fusion of Continuous Annotations
Nicolaou, Mihalis; Pavlovic, Vladimir and Pantic, Maja. 2014. Dynamic Probabilistic CCA for Analysis of Affective Behaviour and Fusion of Continuous Annotations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), pp. 1299-1311.

Conference or Workshop Item

Dynamic Probabilistic Linear Discriminant Analysis For Video Classification
Fabris, Alessandro; Nicolaou, Mihalis; Kotsia, Irene and Zafeiriou, Stefanos. 5-9 Mar 2017. 'Dynamic Probabilistic Linear Discriminant Analysis For Video Classification'. In: Proceedings of IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP). New Orleans, United States 5-9 Mar 2017.

Deep Canonical Time Warping
George, Trigeorgis; Nicolaou, Mihalis; Zafeiriou, Stefanos and Schuller, Bjorn. 2016. 'Deep Canonical Time Warping'. In: Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR'16). Las Vegas, United States.

Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment
Trigeorgis, G.; Snape, P.; Nicolaou, M. A.; Antonakos, E. and Zafeiriou, S.. 2016. 'Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment'. In: Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR'16). Las Vegas, United States.