1339086
Dr Maryam Haghighat
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Maryam Haghighat is a senior lecturer at the QUT School of Electrical Engineering and Robotics. She actively researches across machine learning and computer vision with applications in robotics, healthcare, and remote sensing.Maryam received her PhD from the UNSW School of Electrical Engineering and Telecommunications, Australia. Her doctoral research in image processing at the Interactive Visual Media Processing lab was recognised by the 2020 UNSW Award for Outstanding Doctoral Thesis.
Following her PhD, Maryam joined the Big Data Institute (BDI), Department of Engineering Science, University of Oxford, UK, as a postdoctoral researcher from 2020 to 2022. During her time at Oxford, she contributed to the PathLAKE project funded by InnovateUK, leading the development of machine learning algorithms for medical image analysis.
In 2022, she conducted research jointly across the CSIRO Machine Learning and Artificial Intelligence Future Science Platform, the Mineral Resources and Data61 Research Units in robotic vision and hyperspectral learning.
Maryam is currently a Chief Investigator on several AI projects, including two ARC Discovery Projects, an ARC Research Hub, and an ARC Training Centre, with total funding of over $10 million.
We are seeking highly motivated PhD students and Postdoctoral Researchers with strong Python programming skills and a strong track record in deep learning and/or computer vision.
A selection of available PhD projects can be found here: PhD Topics
We are seeking postdoctoral researchers with expertise in time-series machine learning and a strong track record of publications in leading venues (e.g., ICLR, ICML, and NeurIPS).
For further information and other opportunities, please contact me directly.
Personal details
Positions
- Senior Lecturer
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Machine Learning, Computer Vision, Deep Learning, Artificial Intelligence, Robotics, Signal Processing, Image Processing
Qualifications
- Doctor of Philosophy (University of New South Wales)
Teaching
- EGH444, Digital Signals and Image Processing, Lecturer and Unit Coordinator
- ENN585, Advanced Machine Learning, Lecturer
- ENN595-1/2, Master of Robotics and AI Research Project , Lecturer and Unit Coordinator
Publications
- Haghighat, M., Moghadam, P., Mohamed, S. & Koniusz, P. (2024). Pre-training with Random Orthogonal Projection Image Modeling. Proceedings of the Twelfth International Conference on Learning Representations (ICLR). https://eprints.qut.edu.au/246732
- Haghighat, M., Browning, L., Sirinukunwattana, K., Malacrino, S., Khalid Alham, N., Colling, R., Cui, Y., Rakha, E., Hamdy, F., Verrill, C. & Rittscher, J. (2022). Automated quality assessment of large digitised histology cohorts by artificial intelligence. Scientific Reports, 12(1). https://eprints.qut.edu.au/237203
- Mohamed, S., Haghighat, M., Fernando, T., Sridharan, S., Fookes, C. & Moghadam, P. (2024). FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pre-Training. IEEE Transactions on Geoscience and Remote Sensing, 62. https://eprints.qut.edu.au/245518
- Ramezani, M., Griffiths, E., Haghighat, M., Pitt, A. & Moghadam, P. (2023). Deep Robust Multi-Robot Re-Localisation in Natural Environments. Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3322–3328. https://eprints.qut.edu.au/246126
- Haghighat, M., Mathew, R. & Taubman, D. (2020). Rate-distortion driven decomposition of multiview imagery to diffuse and specular components. IEEE Transactions on Image Processing, 29, 5469–5480. https://eprints.qut.edu.au/237402
- Ali, S., Bailey, A., Ash, S., Haghighat, M., Allan, P., Ambrose, T., Arancibia-Cárcamo, C., Barnes, E., Bird-Lieberman, E., Bornschein, J., Brain, O., Collier, J., Culver, E., Geremia, A., George, B., Howarth, L., Jones, K., Klenerman, P., Palmer, R., Powrie, F., Rodrigues, A., Satsangi, J., Simmons, A., Travis, S., Uhlig, H., Walsh, A., Leedham, S., Lu, X., East, J., Rittscher, J., Braden, B. & other, a. (2021). A Pilot Study on Automatic Three-Dimensional Quantification of Barrett's Esophagus for Risk Stratification and Therapy Monitoring. Gastroenterology, 161(3), 865–878.e8. https://eprints.qut.edu.au/237155
- Haghighat, M., Mathew, R., Naman, A. & Taubman, D. (2019). Illumination Estimation and Compensation of Low Frame Rate Video Sequences for Wavelet-Based Video Compression. IEEE Transactions on Image Processing, 28(9), 4313–4327. https://eprints.qut.edu.au/237413
- Haghighat, M., Mathew, R. & Taubman, D. (2019). Rate-Distortion Driven Separation of Diffuse and Specular Components in Multiview Imagery. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP 2019), 954–958. https://eprints.qut.edu.au/237562
- Haghighat, M., Mathew, R., Naman, A., Young, S. & Taubman, D. (2018). Rate-distortion optimized illumination estimation for wavelet-based video coding. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), 1213–1217. https://eprints.qut.edu.au/237564
- Haghighat, M. & Sadough, S. (2014). Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users. AEU - International Journal of Electronics and Communications, 68(6), 520–527. https://eprints.qut.edu.au/238283
QUT ePrints
For more publications by Maryam, explore their research in QUT ePrints (our digital repository).
Filter publications:
A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/maryam.haghighat
Supervision
Looking for a postgraduate research supervisor?
I am currently accepting research students for Honours, Masters and PhD study.
- Machine Learning for Power Quality Analysis in Low-Voltage Distribution Networks
- Learning complex dynamics from multimodal time-series data
- SLAM inside the human body: camera tracking and 3D reconstruction for medical procedures
- Enhancing 3D visual understanding through multimodal data fusion
- Re-localisation in natural environments
You can browse existing student topics offered by QUT or propose your own topic.
Current supervisions
- PhD, Principal Supervisor
Other supervisors: Professor Clinton Fookes, Associate Professor Simon Denman - Deep Spatial-Spectral Representation Learning for Hyperspectral Data
PhD, Associate Supervisor
Other supervisors: Emeritus Professor Sridha Sridharan, Adjunct Professor Peyman Moghadam, Professor Clinton Fookes, Dr Tharindu Fernando Warnakulasuriya
The supervisions listed above are only a selection.