Imperial College London

Dr Riccardo Secoli

Faculty of Engineering

Technology Transfer Manager



+44 (0)20 7594 3667r.secoli Website




414ABessemer BuildingSouth Kensington Campus





MSc in Computer Engineering (specialisation in control system and automation) in 2006, and PhD in Mechatronics and Industrial System in 2010, both from the University of Padova (Italy). In 2009, he was a visiting PhD Student in the Biorobotics Lab (Dept. Mechanical and Aerospace Eng. - University of California Irvine - USA) where he moved to a postdoctoral fellow position until late 2011.

In 2011, he was a Research Associate at Bioengineering Depart. and later, from 2012 in the Mechatronics in Medicine Lab at Dept. Mechanical Eng. - Imperial College London, till late 2020.

In 2018/2019, he was an Imperial College Techcelerate fellow, and in the period 2019/2021, a Medtech Superconnector Fellow

In August 2020, he graduated with an Executive MBA from Quantic School of Business and Technology (Washington DC - USA)

Since Nov 2020, he is the Technology Transfer Manager at the Hamlyn Centre for Robotic Surgery and a honorary Research Associate at the Department of Mechanical Engineering

Main Research Topics: medical robotics: bio-inspired minimally invasive surgical systems, assistive devices for rehabilitation. Frugal innovation.

Other roles:

  • Since 2018 - Postdoc Rep in the Mechanical Engineering Department and from
  • Since 2020 - a member of Equality and Departmental Culture Committee for the Mechanical Engineering Department
  • Since 2020 - a member of the Health & Safety Committee of the Mechanical Engineering Department.
  • Coordinator for the robotic theme of the European Project: EDEN2020 project.
  • Since 2012 - ICON consultant



Pinzi M, Watts T, Secoli R, et al., 2021, Path Replanning for Orientation-Constrained Needle Steering, Ieee Transactions on Biomedical Engineering, Vol:68, ISSN:0018-9294, Pages:1459-1466

Riva M, Sciortino T, Secoli R, et al., 2021, Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples, Cancers, Vol:13

Favaro A, Secoli R, Rodriguez y Baena F, et al., 2020, Model-Based Robust Pose Estimation for a Multi-Segment, Programmable Bevel-Tip Steerable Needle, Ieee Robotics and Automation Letters, Vol:5, ISSN:2377-3766, Pages:6780-6787


Favaro. A, Secoli R, Rodriguez y Baena F, et al., Optimal pose estimation method for a multi-segment, programmable bevel-tip steerable needle, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), IEEE

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