Emanuele Schiavi
Titular de Universidad
5
Quinquenios
2021
1
Docentia
2010-11
4
Sexenios investigación
2019

Centro

E.S. CC. Experimentales y Tecnología

Departamento

Matemática Aplicada, Ciencia e Ingeniería de los Materiales y Tecnología Electrónica

Área

Matemática Aplicada
Información general
Información general
Presentación
  • Short Bio He was born in Genoa, Italy, 1964 and he is living in Madrid, Spain, since 1989 where he lectures Mathematics at the University Rey Juan Carlos of Madrid. Degrees 1991 B.A Mathematical Sciences. University of Genoa, Italy
    1993 B.A Mathematical Sciences. Universidad Complutense de Madrid 1997 Ph. D. in Applied Mathematics. Thesis:
    On some quasilinear PDE appearing in Glaciology. Mention of European Doctor. Applied Mathematics Department. Universidad Complutense de Madrid.
    Academic Career . He is currently Tenured Professor in Applied Mathematics. Universi- dad Rey Juan Carlos, Madrid, Spain (27-07-2000). He has been Assistant Director at the Escuela Superior de Ciencias Experimentales y Tecnologia (2003-2011) Assistant Director of Research at the Biometrics and Medical Image Analysis Laboratory (2006) Researcher of the Neuroimage laboratory at the Biomedical Technology Center (CTB) of the Universidad Politecnica de Madrid Research advisor of the Neuroimage Laboratory at the Hospital Fundacion Reina Sofia de Madrid. Alzheimer Project.

    Research interest Applied: Medical Images processing. Medical Images, Modelling, Mathe- matical Analysis and Numerical Resolution of Digital Images Filtering and Denoising through Variational techniques. Classification and Segmentation. Optical Flow, 3D image Compression by Wavelet. Medical Images Ap- plications. PET, MRI, fMRI, CT, US modalities. Modelling Geophysical Fluid Dynamics. Modelling geophysical fluids. Unstable Mechanisms in flu- ids flows. Newtonian fluids flows: applications to Geomorphology. Non Newtonian geophysical fluids: applications to paleoclimatology and Glaciol- ogy. Computational Fluid Dynamics: Modelling Polimerization and Flu- idization. Reactors and Bioreactor modelling. Numerical simulation of poly- meric flows. Theoretical Degenerate Parabolic Equations: Doubly Degen- erated Parabolic PDE. The p-laplacian Operator. Total Variation Minimiza- tion Models. Quasilinear Elliptic Equations: Quasilinear Elliptic equations. Degeneration and Singularity. Multigrid numerical Methods. Digital and Medical Image Processing. Neuroimages Processing. Free Boundary Prob- lems: Multivalued Formulations. Complementary Formulations. Variational Inequalities. Bilateral Obstacle Modelling.: 

Méritos
Docencia y asignaturas impartidas en el curso actual

  • Máster universitario oficial

    PLAN ASIGNATURA
    (6230) MÁSTER UNIVERSITARIO EN VISIÓN ARTIFICIALFUNDAMENTOS MATEMÁTICOS
    (6230) MÁSTER UNIVERSITARIO EN VISIÓN ARTIFICIALINTRODUCCIÓN A LA INVESTIGACIÓN EN VISIÓN ARTIFICIAL
HISTÓRICO DOCENTE (ÚLTIMOS 10 CURSOS ACADÉMICOS)
Listado de proyectos (Últimos 10 años)
Códigos de investigador
Publicaciones
      • A non-smooth non-local variational approach to saliency detection in real timeJournal of Real-Time Image Processing

        2021 | journal-article
        Source:Emanuele SchiaviviaScopus - Elsevier
      • Bayesian capsule networks for 3D human pose estimation from single 2D imagesNeurocomputing

        2020 | journal-article
        Source:Emanuele SchiaviviaScopus - Elsevier
      • Non-convex non-local reactive flows for saliency detection and segmentationJournal of Computational and Applied Mathematics

        2020 | journal-article
        Source:Emanuele SchiaviviaScopus - Elsevier
      • Modelling sparse saliency maps on manifolds: Numerical results and applicationsSEMA SIMAI Springer Series

        2019 | book-chapter
        Source:Emanuele SchiaviviaScopus - Elsevier
      • Non-convex non-local flows for saliency detectionarXiv

        2018 | other
          • EID: 2-s2.0-85093157345
          • Part of ISSN: 23318422
        Source:Emanuele SchiaviviaScopus - Elsevier
      • Optimization of a variational model using deep learning: An application to brain tumor segmentationProceedings -