Mostafa Hadadian Nejad Yousefi, MSc

PhD Student

• room number: 566
• e-mail: m.hadadian [at] rug.nl

Bio

Mostafa is completing his PhD in Computer Science at the University of Groningen, where his research focuses on the ECiDA (Evolutionary Changes in Data Analysis) project. He is also the founder and CEO of CAIDEL (Continuous AI Delivery), a company built to bring ECiDA into practice and help operationalize AI projects more effectively.

His expertise lies at the intersection of cloud-native development and machine learning, with a strong emphasis on MLOps. Alongside his academic work, Mostafa has led teams in delivering distributed big data solutions across sectors such as water distribution, banking, insurance, and automotive. His deep knowledge of distributed systems allows him to bridge the gap between academic theory and real-world application.

Research

• Machine Learning Operations (MLOps)
• Big Data
• Distributed Systems
• Cloud Computing
• Machine Learning

Bio

Recent publications

1. DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks (V. Degeler, M. Hadadian, E. Karabulut, H. A. Lazovik, H. van het Loo, A. Tello and H. Troung), In , 2024.

   BibTeX

   
   @article{degeler2024ditec, title={DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks}, author={V. Degeler and M. Hadadian and E. Karabulut and H. A. Lazovik and H. van het Loo and A. Tello and H. Troung}, year={2024} }

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2. DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks (V. Degeler, M. Hadadian, E. Karabulut, A. Lazovik, H. Loo, A. Tello and H. Truong), In Leveraging Applications of Formal Methods, Verification and Validation. Application Areas, Springer Nature Switzerland, 2024.

   Abstract

   Conventional digital twins (DT) for critical infrastructures are widely used to model and simulate the system's state. But fundamental environment changes bring challenges for DT adaptation to new conditions, leading to a progressively decreasing correspondence of the DT to its physical counterpart. This paper introduces the DiTEC system, a Digital Twin for Evolutionary Changes in Water Distribution Networks (WDN). This framework combines novel techniques, including semantic rule learning, graph neural network-based state estimation, and adaptive model selection, to ensure that changes are adequately detected, processed and the DT is updated to the new state. The DiTEC system is tested on the Dutch Oosterbeek region WDN, with results showing the superiority of the approach compared to traditional methods.

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   BibTeX

   
   @inproceedings{victoria2024:ditec, title = "DiTEC: Digital Twin for Evolutionary Changes in Water Distribution Networks", abstract = "Conventional digital twins (DT) for critical infrastructures are widely used to model and simulate the system's state. But fundamental environment changes bring challenges for DT adaptation to new conditions, leading to a progressively decreasing correspondence of the DT to its physical counterpart. This paper introduces the DiTEC system, a Digital Twin for Evolutionary Changes in Water Distribution Networks (WDN). This framework combines novel techniques, including semantic rule learning, graph neural network-based state estimation, and adaptive model selection, to ensure that changes are adequately detected, processed and the DT is updated to the new state. The DiTEC system is tested on the Dutch Oosterbeek region WDN, with results showing the superiority of the approach compared to traditional methods.", author = "V. Degeler and M. Hadadian and E. Karabulut and A. Lazovik and H. Loo and A. Tello and H. Truong", year = "2024", month = oct, language = "English", doi = "10.1007/978-3-031-75390-9_5", url = "https://link.springer.com/chapter/10.1007/978-3-031-75390-9_5", publisher = "Springer Nature Switzerland", booktitle = "Leveraging Applications of Formal Methods, Verification and Validation. Application Areas", }

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   urldoi
3. Self-adaptive service selection for machine learning continuous delivery (M. Hadadian N. Y., V. Degeler and A. Lazovik), In 2024 IEEE International Conference on Web Services (ICWS), 2024.

   BibTeX

   
   @inproceedings{hadadian2024:icws, title={Self-adaptive service selection for machine learning continuous delivery}, author={M. Hadadian N. Y. and V. Degeler and A. Lazovik}, booktitle={2024 IEEE International Conference on Web Services (ICWS)}, year={2024} }

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4. Empowering Machine Learning Development with Service-Oriented Computing Principles (M. Hadadian N. Y., V. Degeler and A. Lazovik), In Symposium and Summer School on Service-Oriented Computing, 2023.

   BibTeX

   
   @inproceedings{hadadian2023:summersoc, title={Empowering Machine Learning Development with Service-Oriented Computing Principles}, author={M. Hadadian N. Y. and V. Degeler and A. Lazovik}, booktitle={Symposium and Summer School on Service-Oriented Computing}, pages={24--44}, year={2023}, organization={Springer Nature Switzerland} }

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5. SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks (S. Akbarinejad, M. Hadadian N. Y. and M. Goudarzi), In BMC bioinformatics, BioMed Central London, volume 22, 2021.

   BibTeX

   
   @article{akbarinejad2021:bmcbio, title={SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks}, author={S. Akbarinejad and M. Hadadian N. Y. and M. Goudarzi}, journal={BMC bioinformatics}, volume={22}, number={1}, pages={335}, year={2021}, publisher={BioMed Central London} }

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(For more publications go to Mostafa's publication page)