I am a lecturer in the Department of Aeronautics at Imperial College London. Prior to joining Imperial, I was a Postdoctoral Associate first at the University of Arizona (Tucson, AZ, USA) and subsequently at the Colorado Center for Astrodynamics Research of the University of Colorado (Boulder, CO, USA). I completed my doctoral studies at the Technical University of Madrid (Spain) in 2017 while working as an Early Stage Researcher in the Stardust Marie Curie Initial Training Network. I obtained a MSc (2013) and BSc (2009) from the University of Naples "Federico II" (Italy), both in Aerospace Engineering.
The overarching theme of my research is the synergy between novel computational approaches and rigorous analytical methods rooted in celestial mechanics and dynamical systems; that is, computational astrodynamics. My research aims to preserve access to space for future generations through the mitigation of space debris and increased spacecraft autonomy.
My primary interests are in the dynamics of objects in the Earth-Moon-Sun system, deep learning applied to large-scale problems in astrodynamics, and robust spacecraft guidance, navigation, and control. Currently, I am working on mathematical methods for Space Situational Awareness and on novel guidance algorithms for Mars Entry, Descent, and Landing.
Potential PhD opportunities
I am always looking for outstanding, highly motivated PhD students for PhDs on advanced topics in astrodynamics. Prospective students are welcome to email me providing:
- Information on a proposed funding source and duration of funding.
- Evidence of a strong background in one or more of the following disciplines: celestial mechanics, dynamic optimisation/state estimation, machine learning (in particular regression problems).
- A technical report, written in English, concerning a project in one of the above disciplines. For example, this can be your Master's dissertation, final year project, conference/journal paper in which you are the lead author.
- Evidence of excellence in an MEng/MSc in Aerospace Engineering or related disciplines.
Due to the large volume of queries, I cannot guarantee a reply if the above information is not provided.
Amato D, McMahon JW, 2021, Deep learning method for Martian atmosphere reconstruction, Journal of Aerospace Information Systems, Vol:18, ISSN:2327-3097, Pages:1-1
et al., 2021, Space occupancy in low-earth orbit, Journal of Guidance Control and Dynamics, Vol:44, ISSN:0731-5090, Pages:684-700
et al., 2019, Non-averaged regularized formulations as an alternative to semi-analytical orbit propagation methods, Celestial Mechanics and Dynamical Astronomy, Vol:131, ISSN:1572-9478, Pages:21-21
et al., 2020, Mars EDL and aerocapture guidance under dynamic uncertainty, AAS/AIAA Astrodynamics Specialist Conference