Our inclusive impact

bridgeUkraine is dedicated to empowering Ukrainian researchers and academics as they navigate the challenges posed by the ongoing conflict. Our mission is to provide vital support and resources that enable researchers to continue their work, contribute to global scientific advancements, and rebuild their careers amidst adversity. Since its inception, bridgeUkraine has facilitated numerous opportunities for Ukrainian researchers, including access to fellowships, international collaborations, tailored training programs, and career development workshops. Ukrainian researchers have published groundbreaking studies, secured prestigious fellowships, and made significant contributions to the scientific community.

Acknowledgment and Testimonial

Recognising BridgeUkraine’s Exceptional Contributions to Capacity Building and Reconstruction Efforts in Ukraine

Issued by: The National Institute for Development and Innovation (NIDI) of the Ministry of Restoration:

Impact stories from Ukrainian researchers

Hello, my name is Ivan Izonin, and I would like to share how the bridgeUkraine initiative has had a lasting impact on both my research activities and my future self-development.

Participating in this organization has allowed me to connect with a wide range of accomplished researchers from diverse fields. It has provided an invaluable opportunity to deepen my understanding of the challenges and issues within civil engineering and, together with my colleagues, to initiate new interdisciplinary research directions aimed at improving the effectiveness of addressing today’s most pressing problems.

The experience gained through bridgeUkraine has enabled me to apply my expertise in artificial intelligence to tackle a broad spectrum of practical challenges in civil engineering. I believe that the collaborative efforts we have started will lay the foundation for large-scale scientific projects. The outcomes of these projects will be pivotal in post-war reconstruction, as we work to revitalize critical civil infrastructure using both the cutting-edge technologies developed within Bridge Ukraine and the insights we have already gained.

I am deeply grateful to bridgeUkraine for making this transformative experience possible.

Watch the videos below to hear firsthand stories from Ukrainian researchers: whose careers were supported by bridgeUrkaine:

Team

Dr.Nataliya Shakhovska

Professor, Lviv Polytechnic National University, Head of the Department of Artificial Intelligence

AI; big data; database and data warehouse integration; distributed systems; integrated systems

Dr.Khrystyna Myroniuk

Associate Professor, Lviv Polytechnic National University, British Academy/CARA

heating, ventilation, and air conditioning; energy-saving buildings; European standards; resource-saving technologies

Dr.Ivan Izonin

Associate Professor, Lviv Polytechnic National University, Senior Member IEEE, Member ACM & INNS

AI/ML; high-speed computational intelligence; neural-like structures; non-iterative training algorithms; ensemble models; meta

Nadiia Kopiika

Research Fellow, British Academy/CARA

strength and reliability; strengthening; retrofitting, material properties; probabilistic approaches; non-destructive methods

Publications:

1. Kopiika N, Karavias A, Krassakis P, Ye Z, Ninic J, Shakhovska N, Argyroudis S, Mitoulis SA (2025). Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies–a tiered approach. Automation in Construction, 170, 105955, https://doi.org/10.1016/j.autcon.2024.105955 
2. Shakhovska N, Mochurad L, Caro R, Argyroudis S. (2025). Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure. Scientific Reports, 15(1), 47 https://doi.org/10.1038/s41598-024-85026-3 
3. Zanevych Y, Yovbak V, Basystiuk O, Shakhovska N, Fedushko S, Argyroudis S (2024). Evaluation of Pothole Detection Performance Using Deep Learning Models Under Low-Light Conditions, Sustainability, 16(24), 10964; https://doi.org/10.3390/su162410964 
4. Izonin I, Muzyka R, Tkachenko R, Gregus M, Kustra N, Mitoulis SA (2024). An approach toward improvement of ensemble method’s accuracy for biomedical data classification. International Journal of Electrical and Computer Engineeringhttps://ijece.iaescore.com/index.php/IJECE/article/view/35701  
5. Izonin I, Muzyka R, Tkachenko R, Dronyuk I, Yemets K, Mitoulis S-A. (2024). A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis. Sensors24(15):4762. https://doi.org/10.3390/s24154762 
6. Kopiika N, Ninic J, Mitoulis S (2024). Damage characterisation using Sentinel-1 images, IABSE Symposium: Construction’s Role for a World in Emergency – Manchester, United Kingdom, DOI: 0.2749/manchester.2024.0367 
7. Izonin I, Kazantzi A, Tkachenko R, Mitoulis SA (2024). GRNN-based Cascade Ensemble Model for Non-Destructive Damage State Identification: Small Data Approach. Engineering with Computershttps://doi.org/10.1007/s00366-024-02048-1 
8. Shakhovska N, Yakovyna V, Mysak M, Mitoulis SA, Argyroudis S, Syerov Y. (2024). Real-Time Monitoring of Road Networks for Pavement Damage Detection Based on Preprocessing and Neural Networks. Big Data and Cognitive Computing, 8(10), 136. https://doi.org/10.3390/bdcc8100136 
9. Izonin I, Nesterenko I, Kazantzi AK, Tkachenko R, Muzyka R, Mitoulis SA (2024). Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset. Scientific Reports, 14(1), 24395. https://doi.org/10.1038/s41598-024-73738-5 
10. Myroniuk K, Furdas Y, Zhelykh V, Adamski M, Gumen O, Savin V, Mitoulis SA (2024). Passive Ventilation of Residential Buildings Using the Trombe Wall. Buildings, 14(10), 3154. https://doi.org/10.3390/buildings14103154 
11. Izonin I, Tkachenko R, Mitoulis SA, Faramarzi A, Tsmots I, Mashtalir D. (2024). Machine learning for predicting energy efficiency of buildings: a small data approach. Procedia Computer Science, 231, 72-77 https://doi.org/10.1016/j.procs.2023.12.173