معرفی
Georgios Rizos is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology within the School of Technology. He holds positions as Postdoctoral Associate at Jesus College and Research Associate at the Mobile Systems Research Laboratory under Dr. Cecilia Mascolo. His academic journey includes a PhD in Computing from Imperial College London (completed 2022), MSc with distinction in Biomedical Engineering, and MEng in Electrical and Computer Engineering from Aristotle University of Thessaloniki.
His research focuses on uncertainty-aware deep learning with applications spanning healthcare diagnostics, bioacoustics, and social media analysis. Key specializations include robust audio representation learning for mobile-based diagnostics, multimodal uncertainty quantification, and responsible AI development. His work bridges theoretical machine learning advancements with real-world implementations in medical and environmental contexts.
Dr. Rizos' publication portfolio demonstrates consistent contributions to top venues including Interspeech, ICASSP, ACL, and IEEE journals. His research shows clear evolution from social network analysis toward healthcare-focused audio modeling, with growing emphasis on uncertainty quantification across modalities. Recent work integrates sonification techniques for explainable AI and develops novel methods for wildlife vocalization detection in natural habitats.
- President's Scholarship of Imperial College London (EPSRC Grant No. 2021037)
- Blue Skies Track 2nd place award at ICMI 2021
- Travel Grant for Interspeech 2021
- Distinction in MSc Biomedical Engineering
- Mit-based funding for industrial placement during MEng
As an academic contributor, Rizos has served as journal reviewer for IEEE Transactions on Cybernetics and Elsevier journals, while participating in program committees for WWW, ACL, and IEEE conferences. His teaching includes Deep Learning (MSc), Probability and Statistics, and Mathematical Methods at Imperial College. Current research focuses on developing uncertainty-aware models for mobile healthcare diagnostics and bioacoustic monitoring systems at Cambridge's Mobile Systems Research Laboratory.
