Qualifications
Research Interests
Applied statistics and machine learning in environmental domain, Applications of deep neural networks for fine-grained recognition.
Developing new methods for spatio-temporal forecasting, in particular with applications to extreme climate events.
Recent Publications
- Kostyleva O., Paramonov V., Shigarov A. and Vetrova V. (2022) Towards Comparison of Table Type Taxonomies. In 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022 - Proceedings: 1461-1465. http://dx.doi.org/10.23919/MIPRO55190.2022.9803520.
- Maliszewski K., Kolenderska S. and Vetrova V. (2022) Removing parasitic elements from Quantum Optical Coherence Tomography data with Convolutional Neural Networks. In ICML 2022, AI for science workshop.
- Maliszewski KA., Vetrova V., Li H., Kolenderski P. and Kolenderska SM. (2022) Dispersion-contrast imaging using machine learning. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11948 http://dx.doi.org/10.1117/12.2612671.
- Ding N., Vetrova V., Bryan K. and Delaux S. (2021) Deep Learning for Spatiotemporal Anomaly Forecasting: A Case Study of Marine Heatwaves. In ICML 2021 Workshop on Tackling Climate Change with Machine Learning.
- Paramonov V., Shigarov A. and Vetrova V. (2021) Rule Driven Spreadsheet Data Extraction from Statistical Tables: Case Study. In Communications in Computer and Information Science 1486 CCIS: 84-95. http://dx.doi.org/10.1007/978-3-030-88304-1_7.