Contact Dr Miguel Arana-Catania
- Tel: +44 (0) 1234 752913
- Email: Miguel.AranaCatania@cranfield.ac.uk
- ORCID
- Google Scholar
Background
Dr Miguel Arana-Catania is a Research and Teaching Fellow in Causal Inference and Verification in Machine Learning. He is a member of the Human Machine Intelligence Group and teaches several modules of the Applied AI MSc and Autonomous Vehicle Dynamics and Control MSc.
He obtained his MSc and PhD degrees in Theoretical Physics from the Universidad Autónoma de Madrid (UAM) and the Institute for Theoretical Physics (IFT) UAM-CSIC.
He has previously worked in Natural Language Processing at the Alan Turing Institute and at the Department of Computer Science, University of Warwick.
He is a Fellow of Advance HE.
Publications
Articles In Journals
- Varghese A, Arana‐Catania M, Mori S, Encinas‐Oropesa A & Sumner J. (2024). Causal discovery to understand hot corrosion. Materials and Corrosion, 75(12)
- Üstek İ, Arana‐Catania M, Farr A & Petrunin I. (2024). Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction. Earth and Space Science, 11(11)
- Fang Z, Arana-Catania M, van Lier F-A, Outes Velarde J, Bregazzi H, .... (2024). SyROCCo: enhancing systematic reviews using machine learning. Data & Policy, 6
- Kochkina E, Hossain T, Logan RL, Arana-Catania M, Procter R, .... (2023). Evaluating the generalisability of neural rumour verification models. Information Processing & Management, 60(1)
- Arana-Catania M, van Lier F-A & Procter R. (2022). Supporting peace negotiations in the Yemen war through machine learning. Data & Policy, 4(1)
- Arana-Catania M, Lier F-AV, Procter R, Tkachenko N, He Y, .... (2021). Citizen Participation and Machine Learning for a Better Democracy. Digital Government: Research and Practice, 2(3)
- Arana-Catania M, Arganda E & Herrero MJ. (2015). Erratum to: Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC. Journal of High Energy Physics, 2015(10)
- Arana-Catania M, Heinemeyer S & Herrero MJ. (2014). Updated constraints on general squark flavor mixing. Physical Review D, 90(7)
- Arana-Catania M, Heinemeyer S & Herrero MJ. (2013). New constraints on general slepton flavor mixing. Physical Review D, 88(1)
- Arana-Catania M, Arganda E & Herrero MJ. (2013). Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC. Journal of High Energy Physics, 2013(9)
- Arana-Catania M, Heinemeyer S, Herrero MJ & Peñaranda S. (2012). Higgs boson masses and B-physics constraints in Non-Minimal Flavor Violating SUSY scenarios. Journal of High Energy Physics, 2012(5)
Conference Papers
- Platanitis K, Arana-Catania M, Upadhyay S & Felicetti L. (2024). A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
- Zhao R, Arana-catania M, Zhu L, Kochkina E, Gui L, .... (2023). PANACEA: An Automated Misinformation Detection System on COVID-19
- McDonnell C, Arana-Catania M & Upadhyay S. (2023). Autonomous robotic arm manipulation for planetary missions using causal machine learning
- Davies J, Arana-Catania M & Procter R. (2022). Embedding digital participatory budgeting within local government: motivations, strategies and barriers faced
- Arana-Catania M, Kochkina E, Zubiaga A, Liakata M, Procter R, .... (2022). Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
- Dougrez-Lewis J, Kochkina E, Arana-Catania M, Liakata M & He Y. (2022). PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
- Arana-Catania M, Kochkina E, Zubiaga A, Liakata M, Procter R, .... (2022). Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
- Davies J, Arana-Catania M, Procter R, van Lier F-A & He Y. (2021). A mixed-methods ethnographic approach to participatory budgeting in Scotland
- Davies J, Arana-Catania M, Procter R, van Lier F-A & He Y. (2021). Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland
- Arana-Catania M, Procter R, He Y & Liakata M. (2021). Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes