dr inż. Michał Romaszewski

Email: 

Orcid ID: 

0000-0002-8227-929X

Stanowisko: 

adiunkt
Zespół badawczy

Pokój: 

1

Telefon: 

+48 32 2317319 w.201

Zainteresowania: 

Przetwarzanie obrazów i szeregów czasowych, uczenie maszynowe, duże modele językowe.

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Publikacje
Biblio RSS

Submitted

1. Filus, K., M. Romaszewski, and M. Żarski, "Semantic Depth Matters: Explaining Errors of Deep Vision Networks through Perceived Class Similarities", arXiv preprint, Submitted.

In Press

2. Sekuła, P., M. Romaszewski, P. Głomb, M. Cholewa, and Ł. Pawela, "Quantum-aware Transformer model for state classification", International Conference on Computational Science 2025, In Press.

2025

3. Gupta, M. K., M. Romaszewski, and P. Gawron, "Potential of quantum machine learning for processing multispectral Earth observation data", Bulletin of the Polish Academy of Sciences Technical Sciences, vol. 73, issue 5, 08/2025.
4. Romaszewski, M., P. Sekuła, P. Głomb, M. Cholewa, and K. Kołodziej, "Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models", Journal of Artificial Intelligence and Soft Computing Research, vol. 15, issue 3, 03/2025.

2024

5. Romaszewski, M., and P. Sekuła, "Poster: Explainable classification of multimodal time series using LLMs", 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), Warsaw, Poland, 04/2024.
6. Kołodziej, K., M. Cholewa, M. Romaszewski, P. Głomb, W. Koral, and P. Sekuła, "Calibration of Water Demand Network Through a Machine Learned Digital Twin Model", 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), 2024.
7. Kołodziej, K., M. Cholewa, P. Głomb, W. Koral, and M. Romaszewski, "Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials", arXiv preprint arXiv:2410.02772, 09/2024.
8. Cholewa, M., M. Romaszewski, P. Głomb, K. Kołodziej, M. Gorawski, J. Koral, W. Koral, A. Madej, and K. Musioł, "‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors", IEEE Sensors Journal, 06/2024.
9. Romaszewski, M., P. Sekuła, P. Głomb, M. Cholewa, and K. Kołodziej, "Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models", arXiv preprint arXiv:2406.04926, 06/2024.
10. Książek, K., "Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data", Computers in Biology and Medicine, vol. 176, 05/2024.

2023

11. Ciemięga, A., K. Maresz, M. Romaszewski, P. Głomb, P. Krupska-Wolas, and K. Prusik, "Hyperspectral Imaging as a Facile and Non-Destructive Method for Size Analysis of Gold Nanoparticles Deposited on Porous Materials", Particle & Particle Systems Characterization, 2023.
12. Głomb, P., M. Cholewa, W. Koral, A. Madej, and M. Romaszewski, "Detection of emergent leaks using machine learning approaches", Water Supply, vol. 23, issue 6, 05/2023.
13. Książek, K., W. Masarczyk, P. Głomb, M. Romaszewski, I. Stokłosa, P. Ścisło, P. Dębski, R. Pudlo, K. Buza, P. Gorczyca, et al., "The analysis of heart rate variability and accelerometer mobility data in the assessment of symptom severity in psychotic disorder patients using a wearable Polar H10 sensor", medXriv, 08/2023.
14. Książek, K., W. Masarczyk, P. Głomb, M. Romaszewski, I. Stokłosa, P. Ścisło, P. Dębski, R. Pudlo, P. Gorczyca, and M. Piegza, HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor, 2023.

2022

15. Książek, K., P. Głomb, M. Romaszewski, M. Cholewa, B. Grabowski, and K. Buza, "Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation", 21st International Conference on Image Analysis and Processing, vol. 13231, Lecce, Italy, Springer, Cham, 05/2022.

2021

16. Pałka, F., W. Książek, P. Plawiak, M. Romaszewski, and K. Książek, "Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios", Sensors, vol. 21, 03/2021.
17. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, "A dataset for evaluating blood detection in hyperspectral images", Forensic Science International, vol. 320, 2021.

2020

18. Domino, M., M. Romaszewski, T. Jasiński, and M. Maśko, Comparison of surface thermal patterns of horses and donkeys in IRT images [Data set], 2020.
19. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 2020.
20. Domino, M., M. Romaszewski, T. Jasiński, and M. Maśko, "Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images", Animals, vol. 10, issue 12, pp. 2201, 11/2020.
21. Książek, K., M. Romaszewski, P. Głomb, B. Grabowski, and M. Cholewa, "Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks", Sensors, vol. 20, issue Recent Advances in Multi- and Hyperspectral Image Analysis, 11/2020.
22. Głomb, P., and M. Romaszewski, "Anomaly detection in hyperspectral remote sensing images", Hyperspectral Remote Sensing: Theory & Applications: Elsevier, 2020.

2019

23. Grabowski, B., P. Głomb, M. Romaszewski, and M. Ostaszewski, "Unsupervised deep learning approach to hyperspectral anomaly detection", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, 2019.
24. Głomb, P., K. Domino, M. Romaszewski, and M. Cholewa, "Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, pp. p. 121, 2019.
25. Cholewa, M., P. Głomb, and M. Romaszewski, "A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification", IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 467-471, March, 2019.

Strony

Historia zmian

Data aktualizacji: 05/07/2024 - 09:03; autor zmian: Michał Romaszewski (michal@iitis.pl)