I am associate professor attached to École Nationale Supérieure de Physique, Électronique et Matériaux (Phelma), GrenobleINP-UGA and a permenant researcher at GIPSA-lab (Grenoble Images Parole Signal Automatique), attached to ACTIV team research unit.

Dawood Al Chanti recieved the B.E degree in Biomedical Engineering. He has a M.S. degree in Machine Learning and Data Mining from the University of Jean Monnet in Saint-Étienne and the University of Alicante in Spain in 2016. He then received a Doctor of Philosophy PhD degree in Image and Signal Processing from the University of Grenoble Alpes in 2019. After that, he worked at lS2N laboratory, École Centrale de Nantes as a Postdoctoral Researcher in the field of Medical Image Analysis using Deep Learning based Method for almost of two years. He joined Grenoble-INP in 2021 where he is now an Associate Professor in the National School of Physics, Electronics and Materials (Phelma) and a permenant research member of the ACTIV team, pôle PSD at Gipsa-Lab. His research lies at the intersection of computer vision and machine learning for medical-image understanding, with a focus on anomaly detection, uncertainty-aware segmentation, multimodal fusion, and explainable AI in high-stakes clinical and industrial settings. He supervises doctoral research in collaboration with university hospitals and industrial partners, accelerating the transfer of reliable vision technologies to real-world applications.  He is an IEEE Fellow and often serves as program committee member for many Journals (IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Medical Imaging, IEEE Trans. on Affective Computing, IEEE Trans. on  Image Processing, Pattern Recognition Elsevier, Medical Image Analysis Elsevier, Artificial Intelligence In Medicine Elsevier) and Conferences (IEEE WCCI, IPCAI, MICCAI, CVPR).  He also serve as a reviewer for the French National Research Agency (ANR) project finance. At Phelm, he is responsible for advanced courses in image and video analysis, hardware-accelerated deep learning, medical-image processing, machine-learning fundamentals, and artificial intelligence, all taught in English across the BIOMED, SICOM, and Daleth programmes. He also coordinates enterprise relations for final-year SICOM students, strengthening academia–industry links through internships and collaborative projects.

• Computer Vision (Anomaly detection, Semantic segmentation, Localization, Classification).

• Uncertainty Quantification and Explainable Analysis in Deep Learning.

• Deep Learning Strategies: (Semi Supervised Learning, Self-Supervised, Few-Shot Learning).

• Active Learning ( Smart data selection, Exploration and Exploitation).

• Medical Image Analysis and Applications: Segmentation, Pathology detection and localization, Volume Quantifications.

• Hybrid AI:  blending physical models and big data.

• Image, vidéo et vision par ordinateur (3A SICOM, S9, 5PMSIVV3)

• Projet d'apprentissage profond - Accélération Matérielle (3A SICOM, S9, 5PMSPAM1) 

• Introduction to Machine learning and Deep learning  (3A BIOMED, S9,5PMBMLD0)

• Traitement de l'image  (M1 Daleth, S8, VPMDIPR1)

• Dynamic System Analysis (M1 Daleth, S8, VPMDDSA2)

• Traitement d'images et applications médicales (2A BIOMED, S8, 4PMBIPM9)

• Traitement d'image avancé (2A SICOM, S8, 4PMSTIA5)

• Introduction à l'intelligence artificielle (2A SICOM, S7, 4PMSIIA4)

• Projet de programmation (1A PI, S6, 3PMIPPR2)

• Correspondance Relations Entreprises 3A SICOM