PhD student
Lausanne University Hospital (CHUV), University of Applied Sciences and Arts of Western Switzerland (HES-SO Valais-Wallis)
Affiliations
Lausanne University Hospital and Lausanne University, Radiology Department (CHUV)
University of Applied Sciences and Arts of Western Switzerland, Institute of Information systems (HES-SO Valais-Wallis)
CIBM Center for Biomedical Imaging
Contact
Email: nataliia (dot) molchanova (at) hevs (dot) ch / nataliia (dot) molchanova (at) chuv (dot) ch
Phone: +41 21 314 15 37
Postal address: Centre de Recherche en Radiologie (RC7), Rue du Bugnon 46, CH-1011 Lausanne
Offices: Rue Centrale 7, 4th floor, CH-1003 Lausanne
LinkedIn: https://www.linkedin.com/in/nataliia-molchanova-699b201b8/
GitHub: https://github.com/NataliiaMolch
Personal website: http://www.mo-na.online/
Research interests
Applications of machine learning to magnetic resonance imaging, explainable AI.
Short Bio
Nataliia was born in 1997. She received a bachelor degree in Physics with a minor in mathematical modelling at Moscow State University in 2019. That year she started a master program in Computational science and engineering at Ecole Polytechnique Fédérale de Lausanne (EPFL). During the master program, she developed an interest to applications of machine learning to medical imaging, and did several projects at Laboratory of signal processing 5 (LTS5) at EPFL. In the scope of her master program, Nataliia worked at Fondation campus biotech Geneva EEG-BCI facility as a Data science intern. Her Master thesis was done in collaboration with LTS5, Siemens Healthineers and CHUV, and was titled “Deep learning for privacy preserving medical imaging: Generative adversarial networks for anonymous face generation”. She successfully graduated in February 2022.
Since March 2022 Nataliia is working on a PhD thesis funded by Haslerfoundation within the project « Explaining AI decisions in personalized healthcare: towards integration of deep learning into diagnosis and treatment planning (MSxplain) ». Her PhD is focused on developing uncertainty and explainability strategies for AI methods to existing deep learning models for MR image analysis in Multiple Sclerosis diagnosis, such as white matter and cortical lesion detection and paramagnetic rim classification. Her doctarate at the Lausanne University is supervised by Dr. Meritxell Bach Cuadra (CIBM, UNIL-CHUV) and Prof. Henning Müller (HES-SO).