Autonomous Aerial Power-Plant Inspection in GPS-Denied Environments
Published:
UAV trajectory generation and edge computer vision for inspecting energy infrastructure where GPS is unreliable. Funded by the US DOE / NETL.
Posts by Collection
portfolio
Intelligent Crack & Corrosion Detection in Infrastructure using Edge Computer Vision
Published:
Deep-learning crack detection running on-board UAVs for real-time infrastructure inspection.
Deep Generative Modelling of Disease Development in Brain Radiology Scans
Published:
Using deep generative models to understand and visualise how disease progresses in brain imaging. Presented at IEEE BIBM 2024.
Multimodal AI for Eye Health: Explainable, Early Anomaly Detection
Published:
Multimodal machine learning with explainable AI (XAI) for earlier, clinically interpretable detection of eye disease — co-designed with clinicians for low-data settings. (Paper and patent in progress).
publications
A CAM/AM-based Trajectory Generation Method for Aerial Power Plant Inspection in GPS-denied Environments
Published in AIAA SciTech 2020 Forum, 2020
A trajectory-generation method for autonomous power-plant inspection drones operating without GPS, presented at AIAA SciTech 2020.
Recommended citation: Rizia, M. M., Reyes-Munoz, J. A., Ortega, A. G., Choudhuri, A., & Flores-Abad, A. (2020). "A CAM/AM-based Trajectory Generation Method for Aerial Power Plant Inspection in GPS-denied Environments." AIAA SciTech 2020 Forum. https://arc.aiaa.org/doi/abs/10.2514/6.2020-0858
Understanding the Development of Disease in Radiology Scans of the Brain through Deep Generative Modelling
Published in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2024, 2024
Deep generative models that re-paint abnormal brain MRI to normal-appearing scans — so realistic that expert radiologists distinguished them from real scans only 51.67% of the time.
Recommended citation: Rizia, M. M., Xu, C., Roberts, J., Barrett, L., Karunasena, S., Edelstein, S., & Suominen, H. (2024). "Understanding the Development of Disease in Radiology Scans of the Brain through Deep Generative Modelling." 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://ieeexplore.ieee.org/document/10822442/
Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study
Published in MedInfo 2025 — Studies in Health Technology and Informatics (IOS Press), 2025
A machine-learning evaluation study identifying non-invasive, blood-based markers of multiple sclerosis to support earlier, less-invasive detection.
Recommended citation: Vlieger, R., Rizia, M. M., Amjadipour, A., Cherbuin, N., Brüstle, A., & Suominen, H. (2025). "Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study." MedInfo 2025, Studies in Health Technology and Informatics (IOS Press). https://www.medrxiv.org/content/10.1101/2025.04.02.25325148v1
Aerial Close Quarter Inspection
Published in , 2026
title: “Autonomous aerial system for intelligent close-quarter inspection” collection: publications permalink: /publication/2026-aerial-close-quarter-inspection excerpt: ‘A low-cost autonomous UAV that navigates GPS-denied industrial structures using visual-inertial odometry and detects cracks and corrosion onboard (98.44% / 72.33% mAP@50). Open access.’ date: 2026-05-05 venue: ‘International Journal of Intelligent Robotics and Applications’ paperurl: ‘https://link.springer.com/article/10.1007/s41315-026-00537-8’ citation: ‘Reyes-Munoz, J. A., Ortega, A. G., Rizia, M. M., Choudhuri, A., & Flores-Abad, A. (2026). "Autonomous aerial system for intelligent close-quarter inspection." International Journal of Intelligent Robotics and Applications.’ —
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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