Check out Scholar or this BibTeX file for a complete list of publications.

Papers

2024

Nannan Huang, Haytham M. Fayek, and Xiuzhen Zhang.
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias.
In European Chapter of the Association for Computational Linguistics (EACL), Malta, Mar 2024.
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Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, and Xiaojun Chang.
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS.
In International Conference on Learning Representations (ICLR), Vienna, Austria, May 2024.
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2023

Nermine Hendy, Ferdi Kurnia, Thomas Kraus, Markus Bachmann, Marco Martorella, Robin Evans, Manfred Zink, Haytham M. Fayek, and Akram Al-Hourani.
SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications.
TechRxiv, 2023.
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Sio-Iong Ao and Haytham M. Fayek.
Continual Deep Learning for Time Series Modeling.
Sensors, vol. 23, no. 1, 2023.
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Rinaldo Gagiano, Haytham M. Fayek, Maria Myung-Hee Kim, Jennifer Biggs, and Xiuzhen Zhang.
IberLEF 2023 AuTexTification: Automated Text Identification Shared Task – Team OD-21.
Iberian Languages Evaluation Forum (IberLEF), 2023.
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Nannan Huang, Lin Tian, Haytham M. Fayek, and Xiuzhen Zhang.
Examining Bias in Opinion Summarisation through the Perspective of Opinion Diversity.
13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA), 2023. pdf arXiv bib

Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, and Xiaojun Chang.
Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear Regions.
In Genetic and Evolutionary Computation Conference (GECCO), Lisbon, Portugal, Jul 2023.
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Jarry Chen and Haytham M. Fayek.
The Structurally Complex with Additive Parent Causality (SCARY) Dataset.
In 2nd Conference on Causal Learning and Reasoning (CLeaR), 2023.
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Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, Stefania Amici, Nicolas Longepe, Pablo Servidia, Pier Marzocca, Haytham M. Fayek, and Luigi Ansalone.
Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors.
Sensors, vol. 23, no. 6, 2023.
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Emma Jane Pretty, Haytham M. Fayek, and Fabio Zambetta.
A Case for personalised non-player character companion design.
International Journal of Human-Computer Interaction, pp.1-20, 2023.
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Kathiravan Thangavel, Pablo Servidia, Roberto Sabatini, Pier Marzocca, Haytham M. Fayek, Santiago Husain Cerruti, Martin España, and Dario Spiller.
A distributed satellite system for multibaseline at-InSAR: Constellation of formations for maritime domain awareness using autonomous orbit control.
Aerospace, vol. 10, no. 2, 2023.
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Emma Jane Pretty, Renan Martins Guarese, Haytham M. Fayek, and Fabio Zambetta.
Replicability and transparency for the creation of public human user video game datasets.
2nd Workshop DATA4XR, IEEE Virtual Reality (IEEEVR), 2023. pdf arXiv bib

Renan Martins Guarese, Emma Jane Pretty, Haytham M. Fayek, Fabio Zambetta, and Ron Van Schyndel.
Evoking empathy with visually impaired people through an augmented reality embodiment experience.
IEEE Virtual Reality (IEEEVR), 2023.
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In memory of Ron van Schyndel.

Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, Stefania Amici, Sarathchandrakumar Thottuchirayil Sasidharan, Haytham Fayek, and Pier Marzocca.
Autonomous satellite wildfire detection using hyperspectral imagery and neural networks: A case study on australian wildfire.
Remote Sensing, vol. 15, no. 3, 2023.
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Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, and Xiaojun Chang.
PRE-NAS: Evolutionary neural architecture search with predictor.
IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 26-36, Feb 2023.
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Oleg Levinski, Wim J.C. Verhagen, Vincenzo Muscarello, Michael J. Scott, Haytham M. Fayek, and Pier Marzocca.
An innovative high-fidelity approach to structural health monitoring.
In 20th Australian International Aerospace Congress (AIAC), Melbourne, Australia, 2023.
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Halide G. Aydogan, Haytham M. Fayek, Xiuzhen Zhang, Kate E. Niessen, Daniel O. Franke, and Pier Marzocca.
Transfer learning for flight loads estimation by load calibration test data.
In 20th Australian International Aerospace Congress (AIAC), Melbourne, Australia, 2023.
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Halide G. Aydogan, Haytham M. Fayek, Xiuzhen Zhang, Michael Scott, Pier Marzocca and Kate E. Niessen, Daniel O. Franke, and Oleg Levinski.
Loads estimation from calibration test with machine learning.
In AIAA Scitech 2023 Forum, 2023.
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2022

Nermine Hendy, Haytham M. Fayek, and Akram Al-Hourani.
Deep learning approaches for air-writing using single UWB radar.
IEEE Sensors Journal, May 2022.
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Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, and Xiaojun Chang.
PRE-NAS: Predictor-assisted evolutionary neural architecture search.
In Genetic and Evolutionary Computation Conference (GECCO), Boston, USA, pp. 1066–1074, Jul 2022.
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Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham M. Fayek, Savitha Ramasamy, and Arulmurugan Ambikapathi.
Knowledge capture and replay for continual learning.
In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, USA, pp. 337–345, Jan 2022.
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Michael Candon, Marco Esposito, Haytham M. Fayek, Oleg Levinski, Stephan Koschel, Nish Joseph, Robert Carrese, and Pier Marzocca.
Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning.
Mechanical Systems and Signal Processing, Jan 2022.
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Michael Candon, Haytham M. Fayek, Oleg Levinski, Stephan Koschel, and Pier Marzocca.
Recent developments in the implementation of a bidirectional LSTM deep neural network for aircraft operational loads monitoring.
In AIAA SciTech Forum, San Diego, USA, Jan 2022.
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Stephan Koschel, Robert Carrese, Michael Candon, Haytham M. Fayek, Pier Marzocca, and Oleg Levinski.
Data-driven flight load prediction using modal decomposition techniques.
In AIAA SciTech Forum, San Diego, USA, Jan 2022.
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2021

Haytham M. Fayek, Michael Candon, Oleg Levinski, Stephan Koschel, and Pier Marzocca
Deep learning airframe load prediction: A data-driven system for aircraft structural health management.
In 19th Australian International Aerospace Congress (AIAC), Melbourne, Australia, Nov 2021.
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Stephan Koschel, Robert Carrese, Haytham M. Fayek, Pier Marzocca, and Oleg Levinski.
Buffet load prediction via frequency response functions.
In 19th Australian International Aerospace Congress (AIAC), Melbourne, Australia, Nov 2021.
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2020

Haytham M. Fayek and Justin Johnson.
Temporal reasoning via audio question answering.
IEEE/ACM Transactions on Audio, Speech, and Language Processing, Jul 2020.
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Haytham M. Fayek and Anurag Kumar.
Large scale audiovisual learning of sounds with weakly labeled data.
In 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, Jul 2020.
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Haytham M. Fayek, Lawrence Cavedon, and Hong Ren Wu.
Progressive learning: A deep learning framework for continual learning.
Neural Networks, vol. 128, pp. 345–357, May 2020.
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2019

Sharath Adavanne, Haytham M. Fayek, and Vladimir Tourbabin.
Sound event classification and detection with weakly labeled data.
In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), New York, USA, pp. 15–19, Oct 2019.
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2018

Haytham M. Fayek, Lawrence Cavedon, and Hong Ren Wu.
On the transferability of representations in neural networks between datasets and tasks.
In Continual Learning Workshop, 32nd Neural Information Processing Systems (NeurIPS), Montréal, Canada, Dec 2018.
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2017

Haytham M. Fayek.
MatDL: A lightweight deep learning library in MATLAB.
The Journal of Open Source Software, vol. 2, no. 19, pp. 413, Nov 2017.
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Haytham M. Fayek, Laurens van der Maaten, Griffin D. Romigh, and Ravish Mehra.
On data-driven approaches to head-related transfer function personalization.
In Audio Engineering Society (AES) Convention 143, New York, USA, Oct 2017.
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Haytham M. Fayek, Margaret Lech, and Lawrence Cavedon.
Evaluating deep learning architectures for speech emotion recognition.
Neural Networks, vol. 92, pp. 60–68, Aug 2017.
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2016

Haytham M. Fayek.
A deep learning framework for hybrid linguistic-paralinguistic speech systems.
In 2nd Doctoral Consortium at Interspeech, Berkeley, USA, Sep 2016.
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Haytham M. Fayek, Margaret Lech, and Lawrence Cavedon.
On the correlation and transferability of features between automatic speech recognition and speech emotion recognition.
In 17th Annual Conference of the International Speech Communication Association (Interspeech), San Francisco, USA, pp. 3618–3622, Sep 2016.
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Haytham M. Fayek, Margaret Lech, and Lawrence Cavedon.
Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels.
In International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, pp. 566–570, Jul 2016.
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2015

Haytham M. Fayek, Margaret Lech, and Lawrence Cavedon.
Towards real-time speech emotion recognition using deep neural networks.
In 9th International Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, Australia, pp. 1–5, Dec 2015.
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2014

P. N. Q. Nhon, Irraivan Elamvazuthi, Haytham M. Fayek, S. Parasuraman, and M.K.A. Ahamed Khan.
Intelligent control of rehabilitation robot: Auto tuning PID controller with interval type 2 fuzzy for DC servomotor.
Procedia Computer Science, vol. 42, Medical and Rehabilitation Robotics and Instrumentation (MRRI2013), pp. 183–190, Dec 2014.
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Haytham M. Fayek, Irraivan Elamvazuthi, N. Perumal, and Bala Venkatesh.
A controller based on optimal type-2 fuzzy logic: Systematic design, optimization and real-time implementation.
ISA Transactions, vol. 53(5), pp. 1583–1591, Sep 2014.
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Haytham M. Fayek, Irraivan Elamvazuthi, N. Perumal, and Bala Venkatesh.
The impact of DFIG and FSIG wind farms on the small signal stability of a power system.
In 5th International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, pp. 1–6, Jun 2014.
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2013

Haytham M. Fayek and Irraivan Elamvazuthi.
Real-time implementation of a type-2 fuzzy logic controller to control a DC servomotor with different defuzzification methods.
In 18th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, pp. 86–91, Aug 2013.
link bib Runner up Best Young Author.

2012

Haytham M. Fayek and Irraivan Elamvazuthi.
Type-2 fuzzy logic PI (T2FLPI) based DC servomotor control.
Journal of Applied Sciences Research, vol. 8(5), pp. 2564–-2574, 2012.
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Patents

Haytham M. Fayek A. A. Abokela and Antonio John Miller.
Systems and methods for hearing assessment and audio adjustment.
Patent No.: US 11,575,999 B2, 7 Feb 2023.
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Irraivan Elamvazuthi and Haytham M. Fayek.
Method for controlling a high speed DC servomotor that controls a robotic arm for the PCB industry (FT2RC).
Patent No.: MY-191698-A, 7 Jul 2022.
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Theses

Haytham M. Fayek.
Continual deep learning via progressive learning.
PhD Thesis, RMIT University, 2019.
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Haytham M. Fayek.
Systematic design of optimal type II fuzzy logic controllers with applications to wind power.
MSc Thesis, Petronas University, 2014.
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Haytham M. Fayek.
Fuzzy logic based motor control.
BEng Thesis, Petronas University, 2012.
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