Artūrs Ņikuļins

Mg. sc. ing.

Research assistant

Arturs Nikulins has been working at EDI since 2021. He graduated from Riga Technical University, where he obtained a professional master's degree in electronics in 2022, as well as a qualification as a leading electronics engineer.

His professional activities at EDI focus on the development, application, and integration of secure artificial intelligence solutions for medical data processing. His research addresses the identification and prevention of hidden and adverse, including malicious, functions that may negatively affect or manipulate artificial intelligence systems.

The work involves researching and applying explainable artificial intelligence (XAI) methods to detect hidden functions and ensure the secure integration of artificial intelligence into data processing workflows.

ResearchGate

ORCID

Projects

Publications

2026
Differentially Private Federated Learning for Remaining Useful Life Prediction(opens in a new window)

Arturs Nikulins, Kārlis Freivalds, Ivars Namatēvs, Kaspars Sudars, Audris Arzovs, Wilhelm Söderkvist Vermelin, Madhav Mishra, Kaspars Ozols.

Applied Sciences, Vol. 16 (6), p. 2784 (raksta numurs), MDPI

DOI(opens in a new window)
2025
Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning(opens in a new window)

Kaspars Sudars, Ivars Namatēvs, Arturs Nikulins, Kaspars Ozols

Applied Sciences (Switzerland), Vol. 15 (10), MDPI
2025
Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning(opens in a new window)

Sudars, K., Namatevs, I., Nikulins, A., & Ozols, K.

Applied Sciences, Vol. 15 (10), p. 5704, MDPI
2025
Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI(opens in a new window)

Arturs Nikulins, Edgars Edelmers, Kaspars Sudars, Inese Polaka

Journal of Imaging, Vol. 11 (2), p. 55, MDPI
2025
Privacy Auditing in Differential Private Machine Learning: The Current Trends(opens in a new window)

Ivars Namatevs, Kaspars Sudars, Arturs Nikulins, Kaspars Ozols

Applied Sciences, Vol. 15 (2), p. 647, MDPI

Artūrs Ņikuļins

Projects

Publications

Differentially Private Federated Learning for Remaining Useful Life Prediction(opens in a new window)

Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning(opens in a new window)

Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning(opens in a new window)

Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI(opens in a new window)

Privacy Auditing in Differential Private Machine Learning: The Current Trends(opens in a new window)