I am currently a postdoctoral researcher at Wellcome Centre for Integrative Neuroimaging (WIN), at University of Oxford working with a fantastic team of scientists on brain connectivity (cross-species, region-to-region, etc.) models using signal processing and AI. Before this role, I was a Research Fellow in AI-human Teams for Cybersecurity within the School of Computing and Information Systems at The University of Melbourne (Nov. 2023 - March 2024) and a Postdoctoral Researcher at the Department of Computer Science and Software Engineering at The University of Western Australia (Sept. 2021 - Oct. 2023). I also serve editorial team of IEEE Transactions on Image Processing as an Associate Editor. My primary area of research centers around robust learning and estimation, encompassing experience in a variety of projects, including but not limited to developing Visual Positioning Systems, Small Object Detection, Brain Activity Detection, Suicide Prediction, and Diffusion Models.

I had the opportunity to be a Visiting Researcher at Wellcome Centre for Integrative Neuroimaging, at University of Oxford, where I contributed to brain comparative analysis model for joint species analysis. I was also a Visiting Researcher at the School of Mathematics and Statistics at The University of Melbourne, where I was involved in developing Statistical Signal Processing techniques for electronic systems.

I received my PhD from The University of Melbourne, at EEE departmet where my research focused on the application of robust signal and image processing techniques in the realm of brain activity detection (Thesis) under the supervision of Prof. Robin J. Evans and A/Prof. Karim Seghouane. Notable achievements during my doctoral studies include the development of innovative signal processing methods for Canonical Correlation Analysis (CCA), Robust Detectors, Sparse Dictionary Learning (SDL), and Subspace Tracking Systems. I received my MSc and BSc degrees from Amirkabir University of Technology and KN Toosi University of Technology both in electrical and electronic engineering.

Research Interests

Image Processing, Deep Learning, Robust Signal Processing, Computer Vision, Medical Imaging, and Biomedical Data Analysis.

PhD Students

Ashkan Taghipour, PhD student at the Department of Computer Science and Software Engineering, The University of Western Australia. Project: Visual Stories from Free-Form Language with Generative Models.

News

• 2/2025: I will be presenting our technique for Connectivity Gradient estimation in OHBM 2025, Australia.
• 2/2025: IT-RUDA has been accepted for publication in ACM TIST.
• 12/2024: Our Guide paper on small object detection has been accepted in IEEE T-ITS. (Link)
• 10/2024: I have been appointed as AE of IEEE Transactions on Image Processing.
• 9/2024: Our paper on Certified Adversarial Robustness has been accepted in NeurIPS2024. (Link)
• 5/2024: Our paper on Robust and Sparse PCA has been accepted in IEEE TIP. (Link)
• 3/2024: Our paper on suicide risk assesment using XAI framework has been accepted in Scientific Reports. (Link)
• 3/2024: I have been appointed as a Postdoctoral Researcher at Nuffield Department of Clinical Neurosciences, Medical School Devision, University of Oxford.
• 10/2023: I have been appointed as a Postdoctoral Research Fellow working with “CATCH” at CIS School, The University of Melbourne.
• 9/2023: Our survey paper on transformers in small object detection is now online. (Link)
• 8/2023: Our paper on Bayesian Pose Estimation has been accepted in IEEE RA-L. (Link)
• 6/2023: Our paper on Subspace Tracking Systems has been accepted in IEEE TSP. (Link)
• 6/2023: Our paper on Robust Block-Structured Dictionary Learning (RBDL) has been accepted in Pattern Recognition Letters. (Link)
• 4/2023: Our paper on pose estimation has been accepted in Pattern Recognition. (Link)
• 2/2023: Two accepted papers for presentation at ICASSP 2023. (Link1) (Link2)
• 1/2023: Our grant proposal: “Analytics for the Australian Grains Industry (AAGI)” has been successfully accepted for Associate Partnership level.
• 12/2022: We have discussed recent advances in small object detection in our workshop held in conjunction with ACCV2022. (Link)

Selected Publications

A guide to image and video based small object detection using deep learning: Case study of maritime surveillance A.M. Rekavandi, L. Xu, F. Boussaid, A.K. Seghouane, S. Hoefs & M. Bennamoun IEEE Transactions on Intelligent Transportation Systems, 2025 IEEE Xplore Datasets

Certified Adversarial Robustness via Randomized α-Smoothing for Regression Models A.M. Rekavandi, F. Farokhi, O. Ohrimenko & B. Rubinstein NeurIPS, 2024 PDF Python Code

Learning Robust and Sparse Principal Components with the α-Divergence A.M. Rekavandi, A.K. Seghouane, & R.J. Evans IEEE Transactions on Image Processing, 2024 IEEE Xplore Matlab Code

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment H. Tang, A.M. Rekavandi, D. Rooprai, G. Dwivedi, F.M. Sanfilippo, F. Boussaid & M. Bennamoun Scientific Reports, 2024 Nature Python Code

B-Pose: Bayesian Deep Network for Camera 6-DoF Pose Estimation from RGB Images A.M. Rekavandi, F. Boussaid, A.K. Seghouane, & M. Bennamoun IEEE Robotics and Automation Letters, 2023 IEEE PDF Python Code

TRPAST: A tunable and robust projection approximation subspace tracking method A.M. Rekavandi, A.K. Seghouane, & K. Abed-Meraim IEEE Transactions on Signal Processing, 2023 IEEE Xplore Matlab Code

Adaptive brain activity detection in structured interference and partially homogeneous locally correlated disturbance A.M. Rekavandi, A.K. Seghouane, & R.J. Evans IEEE Transactions on Biomedical Engineering, 2022 IEEE Xplore Matlab Code

Robust Subspace Detectors Based on α-Divergence With Application to Detection in Imaging A.M. Rekavandi, A.K. Seghouane, & R.J. Evans IEEE Transactions on Image Processing, 2021 IEEE Xplore Matlab Code