Over the past two decades, my supervision activities have contributed to the emergence of interdisciplinary research bridging artificial intelligence, biomedical imaging, computational pathology, mathematical morphology and multimodal medicine. These works span explainable AI, high-content biomedical imaging, digital pathology, neuroimaging and assistive technologies, with applications ranging from cancer and neurodegeneration to precision medicine and clinical decision support.
21
PhD Students Supervised
5
Current PhD Candidates
20+
Years of Mentorship
AI → Medicine
Research Continuum
Ongoing PhD Supervisions
Spatial Transcriptomics Using Physically Inspired Artificial Intelligence
Topic: Physically inspired AI methods for spatial transcriptomics and computational pathology.
PhD candidate, Sorbonne University — EDITE, ED 130. Start: October 2025. Supervision: 100%.
ComPath: Next Generation Computational Pathomics for Personalized Medicine
Topic: Explainable deep learning integration of computational pathology and spatial transcriptomics.
PhD candidate, Sorbonne University — EDITE, ED 130.
Artificial Intelligence in Neuro-Ophthalmology: From Automated Diagnosis to Large-Scale Dataset Curation
PhD candidate, Sorbonne University — EDITE, ED 130.
Understanding, Preventing, and Compensating Blindness: Machine Learning Facing the Data Challenge
PhD candidate, Sorbonne University — EDITE, ED 130.
Study of the Heterogeneous Progression of Parkinson's Disease Using Artificial Intelligence and Multimodal MRI
PhD candidate, Sorbonne University — Brain, Cognition and Behavior Doctoral School, ED 158.
Recently Graduated PhD Students
Decoding the Black Box: Enhancing Interpretability and Trust in AI for Biomedical Imaging
PhD, Sorbonne University, Paris, France — EDITE, ED 130. Defense: 16 Oct. 2024.
Representation Learning and Data-Centric Approaches in Computational Pathology. Instantiation to Alzheimer’s Disease
PhD, Sorbonne University, Paris, France — EDITE, ED 130. Defense: 18 Sept. 2024.
Research Themes Across Supervised PhDs
Earlier PhD Supervisions
Oumeima LAIFA — 2019
Joint discriminative-generative approach for tumor angiogenesis assessment in computational pathology.
Tumor Angiogenesis Generative AI Computational HistopathologyLamine TRAORE — 2017
Semantic modelling of a histopathology image exploration and analysis tool.
Semantic Medical Imaging Knowledge Representation Digital PathologyBassem BEN CHEIKH — 2017
Graph-based mathematical morphology for spatial organization analysis of histological structures.
Mathematical Morphology Tumor Microenvironment Graph-Based ImagingOlivier MORERE — 2016
Deep learning compact and invariant image descriptors for instance retrieval.
Deep Visual Descriptors Image Retrieval Representation LearningSreetama BASU — 2014
Digital reconstruction of neuronal structures from 3D microscopy data.
3D Microscopy Neuronal Reconstruction Biomedical Image AnalysisAntoine FAGETTE — 2014
Dense crowd analysis and scene understanding.
Crowd Analysis Computer Vision Scene UnderstandingStéphane RIGAUD — 2014
Analysis-synthesis approach for neurosphere modelling under phase-contrast microscopy.
Microscopy Imaging Cell Modeling Image AnalysisHumayun IRSHAD — 2014
Automated mitosis detection in color and multispectral high-content images in histopathology.
Mitosis Detection Breast Cancer Pathology High-Content ImagingAntoine VEILLARD — 2012
Kernel methods for incorporating prior knowledge into support vector machines.
Kernel Methods Support Vector Machines Machine Learning TheoryRoxana Oana TEODORESCU — 2011
Parkinson’s disease prognosis using diffusion tensor imaging feature fusion.
Parkinson’s Disease Diffusion MRI Multimodal PrognosisAdina Eunice TUTAC — 2010
Formal representation and reasoning for microscopic medical image-based prognosis.
Medical Knowledge Representation Breast Cancer Grading Reasoning SystemsNicolas PALLUAT — 2006
Dynamic monitoring using temporal neuro-fuzzy systems.
Neuro-Fuzzy Systems Dynamic Monitoring Intelligent SystemsEugenia MINCA — 2004
Discrete event systems monitoring using fuzzy Petri nets for e-maintenance.
Fuzzy Petri Nets E-Maintenance Discrete Event SystemsRyad ZEMOURI — 2003
Monitoring using dynamic neural networks applied to e-maintenance.
Dynamic Neural Networks Industrial AI Predictive Maintenance
