Stefano Galvan received his MSc and PhD in Computer Science at the University of Verona (Italy). Starting from 2001, he has been working as an experienced computer scientist on challenging academic research projects, contributing as coordinator, lead software developer and system administrator. From 2009 to 2013, he worked in industry as IT manager and consultant, his main role being software developer for embedded systems.
In 2013 he joined the Tribology group at Imperial College. His work as a Research Fellow focuses on the management of European Projects in collaboration with the Mechatronics in Medicine Lab (ACTIVE until 2015 and EDEN2020 starting from April 2016) and the design and development of software for scientific research in collaboration with the Non Destructive Evaluation group.
et al., 2022, Inverse Reinforcement Learning Intra-Operative Path Planning for Steerable Needle, Ieee Transactions on Biomedical Engineering, Vol:69, ISSN:0018-9294, Pages:1995-2005
et al., 2022, Insights into infusion-based targeted drug delivery in brain: perspectives, challenges and opportunities, International Journal of Molecular Sciences, Vol:23, ISSN:1422-0067, Pages:3139-3139
et al., 2021, Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery, Ieee Robotics and Automation Letters, Vol:6, ISSN:2377-3766, Pages:6061-6067
et al., 2021, Path replanning for orientation-constrained needle steering, Ieee Transactions on Biomedical Engineering, Vol:68, ISSN:0018-9294, Pages:1459-1466
Pinzi M, Galvan S, Rodriguez y Baena F, 2019, The adaptive hermite fractal tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints, International Journal of Computer Assisted Radiology and Surgery, Vol:14, ISSN:1861-6429, Pages:659-670