- A new paper has been accepted at the International Joint Conference on Neural Networks (IJCNN 2020) :
« Mapping individual differences in cortical architecture using multi-view representation learning » [RANK A]
- New paper published in Expert Systems with Applications Journal:
« Hyperspectral Imagery Classification based on Semi-Supervised 3-D Deep Neural
Network and Adaptive Band Selection » (Apr 04, 2019) [link][ IF: 4.29 ](Q1)
- New paper published in IEEE JSTARS Journal: « Hyperspectral Imagery Semantic Interpretation based on Adaptive Constrained
Band Selection and Knowledge Extraction Techniques » (2018) [link][IF: 3.39 ](Q1)
- New paper accepted in ATSIP 2018: « Comparative Study of Dimensionality Reduction Methods
for Remote Sensing Image Classification » (2018) [link]
Akrem Sellami is currently a Postdoctoral Researcher at LORIA laboratory. The research work focuses on the deep learning and computer vision.
He was a Postdoctoral Researcher of the Qarma (Machine Learning Team), which is situated within the Systems and Computer Science Laboratory (Laboratoire d’Informatique et Systèmes, a.k.a. LIS), Aix-Marseille University (AMU). The work of the postdoc is carried out jointly with the Banco (Neural Bases of Communication) team, Institute of Neuroscience of Timone (Institut de Neurosciences de la Timone a.k.a. INT ). This project is funded by the Institute of Language, Communication and the Brain (ILCB)
Post-doc research focuses on understanding the relationship between brain and behavior using machine learning models (deep learning, graph Kernels, multi-view learning) and multi-modal brain images.
Since Oct. 2017, he was a Teaching Assistant (ATER) at IUT Institute, Paris Descartes University. He received the Ph.D degree in Signal, Image, Vision (SIV) from the University of Bretagne Loire, IMT Atlantique, in 2017. From 2014, he enjoyed a research scholarship at the Image and Information Processing department (ITI) at Telecom Bretagne.
He teaches various aspects of Deep Learning, Computer Science, Data Science, Graph Theory, and Computer Systems.
His current research include deep learning, artificial intelligence, multiview learning, functional data analysis, hyperspectral image analysis, and neuroscience.