Dr Mauro S. Innocente is an Assistant Professor in Aerospace Engineering and a member of the Centre for Future Transport and Cities (CFTC) at Coventry University. He is the founder and head of the Autonomous Vehicles & Artificial Intelligence Laboratory (AVAILAB) and a Fellow of the Higher Education Academy.

He received his building & structural engineering degree from the National University of the Northeast (Argentina) in 2003; his master’s degree in numerical methods for calculus and design in engineering from the Polytechnic University of Catalonia (Spain) in 2007; his PhD degree in Particle Swarm Optimisation from Swansea University (UK) in 2011; and his Postgraduate Degree in Academic Practice in Higher Education from Coventry University (UK) in 2017.

He worked as an Engineering Assistant at GINSA SA (civil engineering company) in Argentina in 2001-2003, as a Research Assistant in the Analysis & Advanced Materials for Structural Design (AMADE) group at the University of Gerona (Spain) in 2006, as a Research Assistant in the Civil & Computational Engineering Centre (C²EC) at Swansea University (UK) in 2010, as a Research Officer in the Advanced Sustainable Manufacturing Technologies (ASTUTE) project at Swansea University in 2010-2014, as a Research Associate in the Institute of Energy at Cardiff University (UK) in 2014-2015, and joined Coventry University (UK) as a Lecturer in Aerospace Engineering and a member of the CFTC in 2015. He founded the AVAILAB in 2016, and was promoted to Assistant Professor in 2018.

He currently teaches MSc modules in Mathematical Modelling, Optimisation Techniques, and Autonomous Aerospace Vehicle Systems. He has also delivered lectures on AI and Aerospace Structures.

He peer-reviews articles for 18 international journals, including Swarm and Evolutionary Computation, Applied Mathematical Modelling, Structural and Multidisciplinary Optimization, Computers & Structures, Sensors, IEEE Transactions on Power Electronics and IEEE Transactions on Intelligent Transportation Systems. He has evaluated project proposals for the National Commission of Scientific & Technological Research (CONICYT) in Chile.

He is the Principal Investigator (PI) of the 5th LRF-ICON PhD Studentship Award for project entitled:
“Efficiency Enhancement of Class-A Foams by Means of Metal Oxide Nanoparticles for Self-Organising Swarms of Autonomous UAVs to Fight Wildfires”
Funder: LRF International Consortium of Nanotechnologies (grant ICON-2018-45)
fEC: £100,000 (£50,000 award + £50,000 match funding).
Date of award: Jan 2019.
PI: Dr Mauro S. Innocente (Lead Researcher & Main Supervisor).
Co-I: Dr Evangelos Gkanas (Researcher & Co-supervisor).
Co-I: Prof. Guillermo Rein (External Co-Supervisor at Imperial College).
Link 1, Link 2

As Co-I, he was part of the Coventry University team for project entitled:
“Resilient Connected & Autonomous Vehicles (ResiCAV)”
Funder: HORIBA MIRA Ltd (consortium partner of one of the seven winners of UK government Cyber-Security Feasibility Studies competition)
fEC: £10,000.
Date of award: Dec 2019.
PI: Dr Hoang Nga Nguyen
Co-I: Prof Siraj Shaikh
Co-I: Dr Jeremy Bryans
Co-I: Dr Mauro S. Innocente
Link 1, Link 2

As Co-I, he was awarded the Early-Career Researchers Grant for project entitled:
“Optimisable system-level thermal models for power electronic converters”
Funder: EPSRC Centre for Power Electronics (through grant EP/K035304/1).
fEC: £55,000 (fEC for Coventry University: £50,000).
Date of award: Jun 2015.
PI: Dr Daniel J. Rogers (University of Oxford).
Co-I: Dr Mauro S. Innocente.
Link 1, Link 2

His research is concerned with algorithm developments in Particle Swarm Optimisation, and with the development of soft and natural computing, mathematical modelling, swarm robotics, control, and optimisation tools within a comprehensive spectrum of applications such as fire propagation modelling, thermal modelling, optimal design of power converters, structural analysis and design, swarm robotics applied to firefighting, and autonomous AI-enabled UAVs for predictive maintenance. Examples are:

• Adaptive general-purpose Particle Swarm Algorithm.
• Adaptive Surrogate Modelling & Optimisation (ASMO) of problems described by real or synthetic data.
• Simulation-based optimal design tool for power-dense converters using efficient physics-based models.
• Mathematical modelling of wildfire propagation.
• Self-organising swarms of autonomous firefighting drones.
• Efficiency enhancement of class-A foams by means of nanoparticles to improve performance of firefighting drones.
• Autonomous AI-enabled UAVs for predictive maintenance.