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Francesca Raimondi


 I am a Machine Learning Scientist for the project Deep Medicine at the George Institute for Global Health. I am working on large biomedical datasets including multimodal images to solve healthcare problems. I completed my PhD in Signal Processing at Grenoble Alpes University (in the Signal and Image processing unit of GIPSA-lab). I received a BSc in Physics Engineering and a MSc in Mathematical Engineering from Politecnico di Milano, as well as a general engineering degree from Ecole Centrale de Lyon.
My research interests include:
  • Machine learning
  • Deep learning
  • Biomedical images
  • Multidimensional signal processing
  • Tensor analysis

Publication

  • Raimondi, F., Comon, P., Michel, O., and Spagnolini, U., Multidimensional factorization through helical mapping, Signal Processing, 131, 171-180, 2017
  • Raimondi, F. E. D., & Comon, P., Tensor DoA estimation with directional elements, IEEE Signal Processing Letters, 24(5), 648-652, 2017
  • Raimondi, F., Farias, R. C., Michel, O., and Comon, P., Wideband multiple diversity tensor array processing, IEEE Transactions on Signal Processing, 65(20), 5334-5346, 2017
  • Raimondi, F., Comon, P., Michel, O., Sahnoun, S., and Helmstetter, A., Tensor decomposition exploiting diversity of propagation velocities: Application to localization of icequake events, Signal Processing, 2016, 118, 75-88
  • Raimondi, F., Comon, P., & Michel, O., Wideband multilinear array processing through tensor decomposition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2951-2955, 2016
  • Raimondi, F., and Comon, P., Tensor decomposition of polarized seismic waves, In XXVème colloque GRETSI, 2015


Projects

  • Deep Medicine Program (Oxford Martin School)
  • ERC DECODA, funded by the European Research Council. The project has been running from September 2013 to August 2018, and is dedicated to tensor data analysis, with an emphasis on applications to health and environment. My work was aimed at developing tensor tools for source separation and localisation, using multidimensional sensor arrays.


External Links

  • Google scholar: link


Contact

  • Email: francesca.raimondi@georgeinstitute.ox.ac.uk