Dr Annalisa Riccardi

Senior Lecturer

Mechanical and Aerospace Engineering

Personal statement

Annalisa Riccardi received her PhD from the Centre of Industrial Mathematics of the University of Bremen, Germany, in 2012 with a thesis on multi objective multidisciplinary design optimisation techniques for rocket design. After the PhD she joined for 2 years the Advanced Concepts Team of the European Space Agency as research fellow in applied mathematics and computer science, where she continued pursuing her research on optimization, in particular on constraints handling techniques for evolutionary computation, as well as extending her field of expertise to other field of computational intelligence, in particular machine learning algorithms for ordinal regression and extreme learning machine algorithms. Her second appointment as post doc, from 2014 to 2016, has been at the Advanced Space Concepts Laboratory of Strathclyde University where, while continuing working on shape optimisation, optimal control, combinatorial optimisation and surrogate modeling techniques, with applications to the automotive and oil and gas sectors, she started to work also on uncertainty propagation techniques and optimisation under uncertainties.

Her main experience is in machine learning, optimisation techniques and applications to the engineering sector. Her current research interests are in the field of

Natural Language Processing and Understanding in support to space engineering design and operations
Blockchain applications in aerospace
Socio-economic applications of EO data
Explainable AI for industrial decision support
Robust data driven planning, routing and scheduling

This expertise is applied in her role as Associate director of the Intelligent Computational Engineering Laboratory (ICE Lab) at the University of Strathclyde

Publications

Back to staff profile

Publications

Question answering over knowledge graphs for explainable satellite scheduling: Powell Cheyenne, Riccardi Annalisa; Journal of Aerospace Information Systems (2025); https://doi.org/10.2514/1.I011531
Explainable decision support for conjunction risk analysis and the design of robust collision avoidance manoeuvres: Darm Paul, Riccardi Annalisa, Parsonage Ben, Wilson Callum, Vasile Massimiliano; 76th International Astronautical Congress (2025)
Multi-modal machine learning prediction of fleetwide switchgear SF6 escape from historical maintenance records, online monitors and domain expertise: Liu Ting, Irwin Fiona, de la Barba Luis, Holton Allan, Jones Dan, Terret-Hensman Rob, Wilson Gordon, Riccardi Annalisa, Brown Blair David, McArthur Stephen, Stewart Brian, Stephen Bruce; CIGRE 2025 International Symposium (2025)
Crime scene to courtroom : Evaluating Earth observation data as forensic evidence in criminal justice: Rapach Seonaid, Wheate Rhonda, Riccardi Annalisa; 76th International Astronautical Congress (2025)
Head-specific intervention can induce misaligned AI coordination in large language models: Darm Paul, Riccardi Annalisa; Transactions on Machine Learning Research (2025); https://doi.org/10.48550/arXiv.2502.05945
Generating textual explanations for scheduling systems leveraging the reasoning capabilities of large language models: Powell Cheyenne, Riccardi Annalisa; Journal of Intelligent Information Systems Vol 63, pp. 1287-1337 (2025); https://doi.org/10.1007/s10844-025-00940-w

More publications

Research

Back to staff profile

Professional Activities

Stockholm Forum on Peace and Development: Participant; 13/5/2025
Strategic Themes: Applied AI Workshop: Participant; 17/1/2025
2nd International Workshop on AI for Space Sustainability: Member of programme committee; 30/10/2024
Generative AI in Financial Services: Invited speaker; 10/10/2023
Strath Methods series of seminars: Invited speaker; 1/9/2022
An eye from the sky to view impact: Case of satellite imaging: Contributor; 21/9/2020

More professional activities

Projects

UDLA 2527 University of Strathclyde | Srivastava, Jaya: Riccardi, Annalisa (Principal Investigator) Uzonyi, Gary (Co-investigator) Srivastava, Jaya (Research Co-investigator); 01-Jan-2025 - 01-Jan-2029
Understanding and Addressing Historical Injustices with Declassified Satellite Imagery: Riccardi, Annalisa (Academic) Uzonyi, Gary (Academic) Srivastava, Jaya (Post Grad Student); 01-Jan-2025 - 31-Jan-2029
XAI for High Pressure Grinding Rollers: Riccardi, Annalisa (Principal Investigator) Anwar, Ali (Co-investigator); 25-Jan-2025 - 25-Jan-2026
Spatial Open-Source Intelligence (S-OSINT) in the Age of Misinformation and Disinformation: Schippers, Birgit (Principal Investigator) Riccardi, Annalisa (Principal Investigator) O'Donnell, Therese (Principal Investigator) Vasile, Massimiliano (Principal Investigator); Reliable and verifiable sources that can document human rights abuses or violations of international humanitarian law are crucial in legal contexts. Spatial open-source intelligence (S-OSINT), which is information that is gathered from publicly available digital data such as satellite or remote sensing data, has become an increasingly significant resource in human rights lawyering, from investigations and fact-finding missions through to courtroom evidence at trial. S-OSINT can bypass challenges in evidence gathering, especially where access to witnesses or sites is difficult.

Despite its considerable value, the use of S-OSINT raises concerns about its impact on data privacy and security, and about the validity and reliability of open-source information. Specifically, worries about the effects of misinformation—the inadvertent sharing of inaccurate content—and disinformation—the intentional sharing of inaccurate content with the aim of causing harm—can raise doubts about the trustworthiness of S-OSINT.

This project asks what best practice in the use of S-OSINT in legal contexts should look like, and it will create frameworks to prevent misinformation and disinformation from undermining the value of S-OSINT.

By investigating the legal, ethical and engineering challenges of S-OSINT, the project will establish rigorous standards that will inform a best practice framework for engineers, legal professionals, researchers, and other S-OSINT users. Its interdisciplinary approach supports the project’s ambition to advance the development of human-centred, ethical and lawful uses of digital and industrial technologies. With this focus, the project will also enhance the Global Challenges theme ‘Digital, Industry and Space’.

To deliver on its ambition, the project will (1) develop a Strathclyde-led interdisciplinary research programme that will create a best practice framework for the use of S-OSINT; (2) foster interdisciplinary and cross-sector collaborations with external partners, including international governmental and non-governmental organisations, legal professionals, and SMEs; and (3) prepare two external funding applications.; 30-Jan-2025 - 31-Jan-2025
TIC LCPE AM-11 Smart Hammer Advanced Analytics: Stephen, Bruce (Principal Investigator) Riccardi, Annalisa (Co-investigator) Liu, Ting (Research Co-investigator); 01-Jan-2024 - 28-Jan-2025
SF6 Escape Prediction for NGET HV Switchgear: Stephen, Bruce (Principal Investigator) Akartunali, Kerem (Co-investigator) Brown, Blair David (Co-investigator) McArthur, Stephen (Co-investigator) Riccardi, Annalisa (Co-investigator) Stewart, Brian (Co-investigator); 01-Jan-2024 - 27-Jan-2026