Stavros Nousias

Dr. Stavros Nousias is a research scientist with a research focus in computational modelling, machine learning, geometry processing and geometric deep learning and computer vision.

Born and raised in Ioannina, Greece, he completed his education at the University of Patras, earning his Diploma in Electrical & Computer Engineering, M.Sc. in Electronics & Information Processing, and Ph.D. in 2011, 2016, and 2022 respectively.

He participated in multiple European and National research projects assuming progressively the roles of software engineer and systems developer, research engineer, research associate and research lead:

MyAirCoach (EU H2020 Grant agreement ID: 643607),
GamECAR (EU H2020 Grant agreement ID: 732068),
Ageing@Work (EU H2020 Grant agreement ID: 826299),
Take-a-Breath (NSRF 2014-2020 project code:T1EDK - 03832),
CPSoSaware (H2020 Grant agreement ID: 871738),
EnerMan (H2020 Grant agreement ID: 958478)
Warmest( Marie Skłodowska-Curie Actions Grant agreement ID: 777981 )
CoConstruct Collaborative Construction (Innovation Network of the Technical University of Munich)

news

Apr 12, 2025	New preprint on “Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs”!
Aug 15, 2024	New preprint on “Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework”!

latest posts

selected publications

IEEE Access

An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification

Stavros Nousias, Aris S Lalos, Gerasimos Arvanitis, and 5 more authors

IEEE Access, 2018

DOI Bib

@article{nousias2018mhealth,
  title = {An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification},
  author = {Nousias, Stavros and Lalos, Aris S and Arvanitis, Gerasimos and Moustakas, Konstantinos and Tsirelis, Triantafillos and Kikidis, Dimitrios and Votis, Konstantinos and Tzovaras, Dimitrios},
  journal = {IEEE Access},
  volume = {6},
  pages = {11871--11882},
  year = {2018},
  publisher = {IEEE},
  doi = {10.1109/ACCESS.2018.2809611},
  url = {https://ieeexplore.ieee.org/document/8302483},
  category_1 = {MLHM}
}

PLoS ONE

AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations

Stavros Nousias, Evangelia I. Zacharaki, and Konstantinos Moustakas

PLoS ONE, 2020

DOI Bib Code

@article{nousias2020avatree,
  title = {AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations},
  author = {Nousias, Stavros and Zacharaki, Evangelia I. and Moustakas, Konstantinos},
  journal = {PLoS ONE},
  volume = {15},
  number = {4},
  doi = {10.1371/journal.pone.0230259},
  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230259},
  pages = {e0230259},
  year = {2020},
  category_1 = {BME}
}

IEEE TII

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 1 more author

IEEE Transactions on Industrial Informatics, 2020

DOI Bib Code

@article{nousias2020fast,
  title = {Fast mesh denoising with data driven normal filtering using deep variational autoencoders},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Moustakas, Konstantinos},
  journal = {IEEE Transactions on Industrial Informatics},
  volume = {17},
  number = {2},
  doi = {10.1109/TII.2020.3000491},
  url = {https://ieeexplore.ieee.org/document/9110709},
  pages = {980--990},
  year = {2020},
  publisher = {IEEE},
  category_1 = {GDL},
}

IEEE Access

A saliency aware CNN-based 3D model simplification and compression framework for remote inspection of heritage sites

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 4 more authors

IEEE Access, 2020

DOI Bib

@article{nousias2020saliency,
  title = {A saliency aware CNN-based 3D model simplification and compression framework for remote inspection of heritage sites},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Pavlidis, George and Koulamas, Christos and Kalogeras, Athanasios and Moustakas, Konstantinos},
  journal = {IEEE Access},
  volume = {8},
  doi = {10.1109/ACCESS.2020.3023167},
  url = {https://ieeexplore.ieee.org/document/9193917},
  pages = {169982--170001},
  year = {2020},
  publisher = {IEEE},
  category_1 = {GDL}
}

ACM TMM

Deep saliency mapping for 3D meshes and applications

Stavros Nousias, Gerasimos Arvanitis, Aris Lalos, and 1 more author

ACM Transactions on Multimedia Computing, Communications and Applications, 2023

DOI Bib PDF Code

@article{nousias2023deep,
  title = {Deep saliency mapping for 3D meshes and applications},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris and Moustakas, Konstantinos},
  journal = {ACM Transactions on Multimedia Computing, Communications and Applications},
  volume = {19},
  number = {2},
  pages = {1--22},
  year = {2023},
  doi = {10.1145/3550073},
  url = {https://dl.acm.org/doi/10.1145/3550073},
  publisher = {ACM New York, NY},
  category_1 = {GDL}
}

PhD
Patient-specific modelling, simulation and real time processing for respiratory diseases

Stavros Nousias

Department of Electrical and Computer Engineering, School of Engineering, University of Patras, Patras, Greece. , 2022

Abs DOI Bib PDF Slides

Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects more than 300 million more people worldwide, while more than 100 million people will likely have asthma by 2025. The price of asthma varies greatly from nation to nation. Mean yearly cost can be estimated to 1900 EUR in Europe and $3100 in the United States. Managing asthma involves controlling symptoms, preventing exacerbations, and maintaining lung function. Improved asthma control is reduces the risk of exacerbations and lung function impairment while reducing the direct costs of asthma care and indirect costs associated with reduced productivity. Understanding the complex dynamics of the pulmonary system and the lung’s response to disease is fundamental to the advancement of Asthma treatment. Computational models of the respiratory system seek to provide a theoretical framework to understand the interaction between structure and function. Their application can improve pulmonary medicine by a patient-specific approach to medicinal methodologies optimizing the delivery given the personalized geometry and personalized ventilation patterns. A three-fold objective is addressed within this dissertation. The first part refers to the comprehension of pulmonary pathophysiology and the mechanics of Asthma and subsequently of constrictive pulmonary conditions in general. The second part refers to the design and implementation of tools that facilitate personalized medicine to improve delivery and effectiveness. Finally, the third part refers to the self-management of the condition, meaning that medical personnel and patients have access to tools and methods that allow the first party to easily track the course of the condition and the second party, i.e. the patient to easily self-manage it alleviating the significant burden from the health system.
@phdthesis{nousias2022patient, title = {Patient-specific modelling, simulation and real time processing for respiratory diseases}, author = {Nousias, Stavros}, year = {2022}, school = {Department of Electrical and Computer Engineering, School of Engineering, University of Patras, Patras, Greece. }, doi = {10.12681/eadd/52290}, url = {https://www.didaktorika.gr/eadd/handle/10442/52290?locale=en}, category_1 = {BME}, category_2 = {PHD}, category_3 = {MLHM}, }

IEEE Access

AI sound recognition on asthma medication adherence: Evaluation with the RDA benchmark suite

Dimitris Nikos Fakotakis, Stavros Nousias, Gerasimos Arvanitis, and 2 more authors

IEEE Access, 2023

DOI Bib PDF Code

@article{fakotakis2023ai,
  title = {AI sound recognition on asthma medication adherence: Evaluation with the RDA benchmark suite},
  author = {Fakotakis, Dimitris Nikos and Nousias, Stavros and Arvanitis, Gerasimos and Zacharaki, Evangelia I and Moustakas, Konstantinos},
  journal = {IEEE Access},
  volume = {11},
  pages = {13810--13829},
  year = {2023},
  doi = {10.1109/ACCESS.2023.3258400},
  url = {https://ieeexplore.ieee.org/document/10040762},
  publisher = {IEEE},
  category_1 = {MLHM}
}

IEEE Access

Accelerating deep neural networks for efficient scene understanding in multi-modal automotive applications

Stavros Nousias, Erion-Vasilis Pikoulis, Christos Mavrokefalidis, and 1 more author

IEEE Access, 2023

DOI Bib PDF

@article{nousias2023accelerating,
  title = {Accelerating deep neural networks for efficient scene understanding in multi-modal automotive applications},
  author = {Nousias, Stavros and Pikoulis, Erion-Vasilis and Mavrokefalidis, Christos and Lalos, Aris S},
  journal = {IEEE Access},
  doi = {10.1109/ACCESS.2023.3258400},
  url = {https://ieeexplore.ieee.org/document/10073550},
  volume = {11},
  pages = {28208--28221},
  year = {2023},
  publisher = {IEEE},
  category_2 = {MCA}
}

arXiv

Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework

Changyu Du, Sebastian Esser, Stavros Nousias, and 1 more author

arXiv preprint arXiv:2408.08054, 2024

DOI Bib PDF Code

@article{du2024text2bim,
  title = {Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework},
  author = {Du, Changyu and Esser, Sebastian and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {arXiv preprint arXiv:2408.08054},
  year = {2024},
  doi = {10.48550/arXiv.2408.08054},
  url = {https://arxiv.org/abs/2408.08054},
  category_1 = {AIBE},
}

Frontiers

Distributed intelligence in industrial and automotive cyberphysical systems: A review

Nikos Piperigkos, Alexandros Gkillas, Gerasimos Arvanitis, and 6 more authors

Frontiers in Robotics and AI, 2024

DOI Bib

@article{piperigkos2024distributed,
  title = {Distributed intelligence in industrial and automotive cyberphysical systems: A review},
  author = {Piperigkos, Nikos and Gkillas, Alexandros and Arvanitis, Gerasimos and Nousias, Stavros and Lalos, Aris and Fournaris, Apostolos and Radoglou-Grammatikis, Panagiotis and Sarigiannidis, Panagiotis and Moustakas, Konstantinos},
  journal = {Frontiers in Robotics and AI},
  doi = {10.3389/frobt.2024.1430740},
  url = {https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1430740},
  volume = {11},
  year = {2024},
  publisher = {Frontiers},
  category_1 = {CPS}
}