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General Information

Full Name Simone Saitta
Date of Birth 12th July 1993
Languages Italian (mother tongue), English (spoken and written fluently), Spanish (beginner)

Experience

  • Nov. 2019 - May 2023
    Ph.D. in Bioengineering
    Politecnico di Milano, Milan, Italy
    • Development, training and application of deep neural networks for automatic 3D segmentation of medical images.
    • Integration of deep neural networks within computational pipelines for mixed-reality applications in neurosurgery.
    • Development of data-driven statistical shape models of hemodynamics from flow-encoded magnetic resonance images.
    • Development of deep neural network-based methods for unsupervised 4D reconstruction of fluid flow from flow-encoded MRI.
    • Development of a 4D variational data assimilation framework for blood flow dynamics in cerebral aneurysms.
    • Gained programming experience with the FEniCS API for PDE-constrained optimization using an adjoint solver.
  • Mar. 2022 - Aug. 2022
    Visiting Ph.D. Student
    University of Cambridge, Cambridge, UK
    • Increased knowledge of inverse problems in medical imaging applications.
    • Development of an unsupervised learning approach for denoising and super-resolution of flow-encoded MRI.
  • 2022
    Consultant
    Politecnico di Milano, Milan, Italy
    • Development of user-defined subroutines for particle-laden numerical simulations of fluid flow.
    • Numerical modeling of multi-phase and multi-scale flows in complex geometries using a volume-of-fluid method.
  • 2019
    Research Engineer
    National University of Singapore, Singapore
    • Development of an automatic image registration computational framework based on iterative optimization methods.
    • Implementation of automatic multi-view 4D image registration using python and simple-elastix.
    • Gained programming experience with Python and C++ cross-platform libraries for object-oriented libraries for image processing.
  • 2018
    Research Assistant
    Imperial College London, London, UK and Zhongshan Hospital, Shanghai, China
    • Optimization of a finite element-based framework for non-invasive pressure calculation from magnetic resonance flow images.
    • Implementation of a user-friendly GUI for medical image processing.
    • CFD simulation of blood fluid dynamics in aortic dissections using the open-source finite element software SimVascular.
    • Gained programming experience in the MATLAB environment.
  • 2017
    MSc Thesis Project
    Imperial College London, London, UK
    • MATLAB implementation of a finite element-based framework for solving the pressure Poisson equation from magnetic resonance flow images.
    • Validation of numerical results with a 3D printed hydraulic mock-loop.
    • Development and setup of fluid-structure interaction and computational fluid dynamics simulations and applications to pathological blood flow assessment.
  • 2017
    MSc Course Project
    Politecnico di Milano, Milan, Italy
    • Setup of CFD simulations of blood flow using Ansys Fluent.
    • Hemodynamic assessment using two different models for blood viscosity; Newtonian and non-Newtonian (generalized power law).
    • Implementation of user defined routines in Ansys Fluent to model blood viscosity and transient flow boundary conditions.

Technical Skills

  • Programming: Python, MATLAB, C, C++ (beginner).
  • Libraries: Pytorch, VTK, ITK, FeniCS, elastix.
  • Containerization: Docker, Singularity.
  • Miscellaneous: Experience with HPC systems, including GPU-accelerated clusters.
  • Simulation software: Ansys, Star-CCM+, OpenFOAM, SimVascular, CRIMSON.
  • Data processing and visualization software: Paraview, Ensight, Meshmixer, 3DSlicer, SolidWorks.

Grants and Awards

  • 2023
    McKusick Fellowship Research Grant
    $100,000 grant from the Marfan Foundation.
    • Project title: Machine learning for identification of geometrical and biomechanical markers of aortic dissection in Marfan patients.
  • 2022
    Switch2Product startup competition
    €30,000 prize from Politecnico di Milano
    • Project title: IPSE-XR - A deep learning and mixed reality-based solution to support neurosurgery.

Academic Interests

  • Machine learning
    • Computer vision
    • Geometric deep learning
    • Neural representations
  • Numerical simulations
    • Computational fluid dynamics
    • Finite elements and finite volumes
    • Data assimilation

Other Interests

  • Football (or soccer) ⚽, Vipassana meditation 🧘