Rate My Professor Karim Seghouane

Abd-Krim (Karim) Seghouane is an Associate Professor of Mathematical Statistics in the School of Mathematics and Statistics, Faculty of Science, at the University of Melbourne. His research focuses on statistical signal and image processing, machine learning, and artificial intelligence, with applications in medical imaging and physiological signal analysis. He belongs to the Data Science and Statistics research groups within his school. Seghouane received his PhD in Signal Processing and Control from Université Paris-Sud XI, France, in 2003. After his PhD, he worked as a postdoctoral researcher at INRIA Rocquencourt, France, followed by positions as a researcher and senior researcher at National ICT Australia (NICTA), where he also served as an adjunct faculty member in the College of Engineering and Computer Science at the Australian National University. He joined the University of Melbourne as an ARC Future Fellow and Lecturer in the Department of Electrical and Electronic Engineering, progressing to Senior Lecturer before transitioning to his current role in the School of Mathematics and Statistics.

Seghouane has received fellowships from the Australian Research Council, the Japanese Society for the Promotion of Science, the Australian Academy of Science, and the French National Institute for Research in Digital Science and Technology. He serves as a Senior Area Editor for the IEEE Transactions on Image Processing. His key publications include 'Learning Robust and Sparse Principal Components With the α-Divergence' (IEEE Transactions on Image Processing, 2024), 'An α-Divergence Approach To Robust Canonical Correlation Analysis' (ICIP, 2024), 'Robust Deterministic DOA Estimation Using α-divergence in Unknown Noise Fields with Sparse Sensor Arrays' (ICASSP, 2025), 'A Guide to Image- and Video-Based Small Object Detection Using Deep Learning: Case Study of Maritime Surveillance' (IEEE Transactions on Intelligent Transportation Systems, 2025), and 'Sparse Canonical Correlation Analysis With Preserved Sparsity' (IEEE Transactions on Knowledge and Data Engineering, 2026). He supervises postgraduate students and contributes to advancements in neuroimaging, fMRI analysis, and robust statistical methods, influencing signal processing and data science fields.