Dr Giovanni Luca Masala

Dr. Masala has a PhD in Applied Physics (AI in medical applications) in 2006 and a "Laurea" (MSc+BSc degree) in Electronic Engineering (path in AI) in 2002, both at the University of Cagliari, Italy. Before joining the University of Kent, he was a Senior Lecturer and Leader of the Robotics Lab at Manchester Metropolitan University. Previously, he was Lecturer at the University of Plymouth, leading the Big Data group.

He has extensively published his research across ranked journals. In 2019 he obtained a grant funded by EU Project Interreg 2 Seas 2014-2020 “AGE Independently (AGE’IN)“ with the role of PI for the University of Plymouth. In 2018 as Academic Lead (PI) he obtained two Knowledge Transfer Partnership projects, funded by Innovate UK) for Machine learning applications. He also developed project management experience as a co-investigator in the 2014-2015 project “Cloud computing platform for SMEs”, funded by Sardinia Regional Council, and the 2008-2009 project “Disclosure of information, culture and e-business on the Internet: access for persons with disabilities".

He is currently Co-Lead of Future Human  Signature Research Theme, University of Kent and Associate Editor in Computational Intelligence in Robotics (Frontiers in Robotics and AI). He is leading the Cognitive Robotics and Autonomous Systems (CoRAS) lab, at University of Kent.

Giovanni belongs to the following research groups:

• Artificial Intelligence and Data Science Group

Giovanni is interested in a range of topics:

• AI and Computer Vision in Healthcare and assistive technologies
• Cognitive Systems to create the next generation of smart robots
• Human-Robot Interaction(HRI)
• Evolutionary algorithms for mobile robots’ path planning

In the field of language development, he was part of a small group of international researchers,  who developed a very large-scale neural network of cognitive and language processing, called ANNABELL. The publication in PLoS One in 2015 (1) received a high impact both scientifically and in the media, including the UK “The Times”, Daily Mail, Quartz, Wired and other international news websites, popular science and blogs. Altmetrics ranks the attention received by the paper amongst the top 5% of articles of all time.

Currently, he is leading a group of international researchers between the UK and Italy to exploit the architecture on robotics applications (2) and multiple language understanding (3). 

1. Golosio B, Cangelosi A, Gamotina O, MASALA G.L,  (2015). A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866.

2. Giorgi, I, Cangelosi A., and  MASALA G. L (2021) “Learning Actions from Natural Language Instructions using an ON-World Embodied Cognitive Architecture”, in FRONTIERS IN NEUROROBOTICS, doi: 10.3389/fnbot.2021.626380 

3. Giorgi, I. Golosio, B., Esposito, M., Cangelosi A. and MASALA G. L.,(2020) "Modelling Multiple Language Learning in a Developmental Cognitive Architecture," in IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, doi: 10.1109/TCDS.2020.3033963. 

• COMP8160 e-Health(Convenor 2022/2023) 
• COMP6560 Computational Intelligence in Business, Economics & Finance (2021/2022/2023)
• COMP6685 Deep Learning (Convenor 2022/2023) 

Giovanni is looking for students interested in applications that match the research interests: Cognitive models in robotics, HRI and cooperative AI/robotics. He is also interested in a broad range of applications in healthcare (e.g. Computer Aided Detection Systems for radiological, classification of data from wearable devices, etc).

The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and devices like AR Epson glasses and Microsoft HoloLens.

Senior Member IEEE (number 96985445), IEEE Computational Intelligence Society. 

Fellow of The Higher Education Academy UK Recognition reference: PR137587.