STAFF PROFILE
Dr Haytham Fayek
Position:
Senior Lecturer
College / Portfolio:
STEM College
School / Department:
STEM|School of Computing Technologies
Phone:
+61399250858
Email:
haytham.fayek@rmit.edu.au
Campus:
City Campus
Contact me about:
Research supervision
Dr Haytham Fayek is an artificial intelligence and machine learning expert. His research interests are broadly in artificial intelligence, machine learning, machine reasoning, deep learning, continual learning, and machine perception.
Availability by appointment.
- 2019 – Doctor of Philosophy (Ph.D., Electrical and Computer Engineering), RMIT University, Melbourne, Australia.
- 2014 – Master of Science (M.Eng., Electrical and Electronics Engineering), Petronas University of Technology, Malaysia.
- 2012 – Bachelor of Engineering (B.E., Electrical and Electronics Engineering), Petronas University of Technology, Malaysia.
2018-2019 Postdoctoral Research Scientist, Facebook, WA, USA.
For more information, visit:
Homepage: https://haythamfayek.com
- Gopalakrishnan, S.,Singh, P.,Fayek, H.,Ramasamy, S.,Ambikapathi, A. (2022). Knowledge Capture and Replay for Continual Learning In: Proceedings of the 22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022), Waikoloa, Hawaii, 4-8 January 2022
- Candon, M.,Esposito, M.,Fayek, H.,Levinski, O.,Koschel, S.,Joseph, N.,Carrese, R.,Marzocca, P. (2022). Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning In: Mechanical Systems and Signal Processing, 171, 1 - 25
- Candon, M.,Fayek, H.,Koschel, S.,Levinski, O.,Marzocca, P. (2022). Recent Developments in the Implementation of a Bidirectional LSTM Deep Neural Network for Aircraft Operational Loads Monitoring In: Proceedings of the AIAA Scitech 2022 Forum, San Diego, California, United States, 3-7 January 2022
- Koschel, S.,Candon, M.,Fayek, H.,Marzocca, P.,Levinski, O. (2022). Data-Driven Flight Load Prediction using Modal Decomposition Techniques In: Proceedings of the AIAA Scitech 2022 Forum, San Diego, California, United States, 3-7 January 2022
- Hendy, N.,Abokela, H.,Hourani, A. (2022). Deep Learning Approaches for Air-writing Using Single UWB Radar In: IEEE Sensors Journal, 22, 11989 - 12001
- Peng, Y.,Song, A.,Ciesielski, V.,Abokela, H.,Chang, X. (2022). PRE-NAS: predictor-assisted evolutionary neural architecture search In: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States of America, 09/07/2022 -13/07/2022
- Fayek, H.,Kumar, A. (2021). Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan, 7-15 January 2021
- Fayek, H.,Cavedon, L.,Wu, H. (2020). Progressive learning: A deep learning framework for continual learning In: Neural Networks, 128, 345 - 357
- Fayek, H.,Johnson, J. (2020). Temporal Reasoning via Audio Question Answering In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2283 - 2294
- Fayek, H.,Adavanne, S.,Tourbabin, V. (2019). Sound event classification and detection with weakly labeled data In: Proceedings of the 4th Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE 2019), New York, United States, 25–26 October 2019