My research focuses on developing neural networks for computer vision and spatio-temporal forecasting, with primary application to environmental simulation (especially ocean hydrodynamics). I also explore methods in manifold learning for high-dimensional data analysis.
As a specialist in GIS and cartography, I bridge data science with geospatial visualization, creating maps and infographics to interpret model outputs.
I am the part of the NSS Lab team. My key projects include TorchCNNBuilder, a Python library for modular and configurable construction of convolutional neural networks in PyTorch, designed to streamline prototyping and experimentation, and contributions to the automated machine learning framework FEDOT. I am driven by building tools that bridge advanced AI with impactful domain science.
My research papers are available on Google Scholar.