Telecom SudParis | France
Ms. Houda Hassini is an interdisciplinary data scientist whose research bridges deep learning, computer vision, biomedical imaging, and remote-sensing technologies, making her a powerful emerging voice at the intersection of artificial intelligence and advanced materials characterization. With a strong academic foundation spanning mathematics, computer science, and artificial-intelligence engineering, she has developed specialized expertise in multimodal imaging, physics-informed machine learning, optical flow modeling, generative adversarial networks, and high-resolution computational imaging. As a Postdoctoral Researcher at ONERA – The French Aerospace Lab, she focuses on designing advanced deep-learning pipelines for sea-ice drift estimation using multimodal satellite imagery, combining synthetic aperture radar, multispectral optical data, and physics-guided simulation strategies to achieve more accurate environmental monitoring under complex real-world conditions. Her doctoral research at Télécom SudParis introduced innovative physics-inspired generative models for biomedical imaging, particularly within Fourier Ptychographic Microscopy, where she developed end-to-end pipelines capable of significantly improving rare blood-cell classification and enhancing phase-amplitude coupled imaging for automated diagnostics. Ms. Hassini has contributed to several high-visibility scientific projects in collaboration with clinical partners and industry teams, and her work has been published in peer-reviewed journals and major conferences in optics, photonics, and computational imaging. She has expertise in statistical modeling, clustering, attention-based architectures, unsupervised learning, and large-scale data processing using Python, R, modern deep-learning frameworks, and GPU-accelerated computing environments. In addition to her research achievements, she has taught and supervised students in applied mathematics, signal processing, biomedical data science, and AI-based image analysis across leading French academic institutions. Multilingual and globally engaged, Ms. Hassini also maintains active involvement in community leadership and scientific outreach. Her innovative contributions, interdisciplinary impact, and commitment to advancing imaging intelligence technologies make her an exceptional candidate for the Interdisciplinary Research Award.
Featured Publications
Hassini, H., Dorizzi, B., Leymarie, V., Klossa, J., & Gottesman, Y. (2025). Enhancing classification of rare white blood cells in FPM with a physics-inspired GAN. Scientific Reports, 15(1), 42310.
Hassini, H., Dorizzi, B., Thellier, M., Klossa, J., & Gottesman, Y. (2023). Investigating the joint amplitude and phase imaging of stained samples in automatic diagnosis. Sensors, 23(18), 7932.
Hassini, H., Dorizzi, B., Klossa, J., & Gottesman, Y. (2024). Optimization of FPM using phase images for malaria detection. In Proceedings of SPIE Photonics Europe (Paper 12996-10). SPIE.
Hassini, H., Bouchama, L., Sextius, M., Klossa, J., Fleury, C., Baran-Marszak, F., Delhommeau, F., Dorizzi, B., Gottesman, Y., & Leymarie, V. (2024). The TAMIS project: Hematology cytology and artificial intelligence: Revolution is in the light bulb. ISLH Proceedings. (Accepted).
Hassini, H., Bouchama, L., Sextius, M., Klossa, J., Fleury, C., Baran-Marszak, F., Delhommeau, F., Dorizzi, B., Gottesman, Y., Leymarie, V., & others. (2024). The TAMIS project: Hematology cytology and artificial intelligence: Revolution is in the light bulb. Presented at the SFH — 43rd Annual Congress, Paris, France.
Hassini, H., Niang, N., & Audigier, V. (2021). SOM-based clusterwise regression. In DSSV 2021: Data Science, Statistics & Visualization Conference, Rotterdam.