N. (Nick) Eleftheroglou PhD
N. (Nick) Eleftheroglou PhD
Profile
Biography
Dr. Nick Eleftheroglou is an Assistant Professor and the Head of the Intelligent Sustainable Prognostics (iSP) Group in the Faculty of Aerospace Engineering at Delft University of Technology (TU Delft). Nick received his diploma in Mechanical and Aeronautics Engineering cum laude (8.93/10) from University of Patras (Greece) in 2015. After that, he obtained his PhD cum laude at TU Delft in October 2020. Afterwards, he worked for two years as a postdoctoral researcher and business consultant.
Expertise
Nick's research focuses on Sustainable AI for Diagnostics, Prognostics, and Health Management of structures and engineering systems. His aim is to develop novel interpretable physics-informed AI models able to reduce the related AI carbon emissions, along with improving their reliability and robustness. To that end, his research interests are in the areas of data analysis, diagnosis, and prognosis of systems and structures utilizing machine learning techniques, stochastic models, Bayesian statistics, and health/condition monitoring techniques. In recent years, he has significantly contributed to adaptive prognostics of aeronautical structures, developing novel probabilistic Machine Learning algorithms.
Nick’s ultimate goal is to improve safety, reliability, and availability in aviation and other sectors, through Sustainable physics-informed AI methods.
Awards
In 2020, Nick obtained the prestigious cum laude distinction, only awarded to the top 5% of doctoral researchers, for his Ph.D. thesis "Adaptive prognostics for remaining useful life of composite structures". He also received several awards and scholarships in his undergraduate education from the Hellenic National Scholarship Foundation and the Euro Bank EFG.
Keywords
Prognostics and Health Management, AI, Diagnostics, Data Mining, Machine Learning, Stochastic Models, Bayesian Statistics, Data Fusion, Markov Models, Physics-Informed models, Structures
Expertise
Publications
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2024
A robust generalized deep monotonic feature extraction model for label-free prediction of degenerative phenomena
Panagiotis Komninos / Thanos Kontogiannis / Dimitrios Zarouchas / Nick Eleftheroglou
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2024
Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning
P. Komninos / A.E.C. Verraest / N. Eleftheroglou / D. Zarouchas
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2024
Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures
Nick Eleftheroglou / Georgios Galanopoulos / Theodoros Loutas
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2023
A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures
Georgios Galanopoulos / Nick Eleftheroglou / Dimitrios Milanoski / Agnes Broer / Dimitrios Zarouchas / Theodoros Loutas
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2023
Acoustic emission-based remaining useful life prognosis of aeronautical structures subjected to compressive fatigue loading
Georgios Galanopoulos / Dimitrios Milanoski / Nick Eleftheroglou / Agnes Broer / Dimitrios Zarouchas / Theodoros Loutas
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