About

Ilia Bagov is a Research Associate with prior experience in machine learning in areas such as remote sensing, finance, recommender systems, and more. He specializes in the convergence of FAIR (Findable, Accessible, Interoperable, and Reusable) [1] data principles within the realm of experimental sciences. Ilia has played a key role in leading software development teams responsible for the creation of innovative applications, including VocPopuli [2] and the FAIR-Save suite of solutions, designed specifically to annotate and generate FAIR-compliant data in experimental settings.

Currently, Ilia is focused on the development of a comprehensive framework that seamlessly integrates FAIR datasets into machine learning algorithms. This undertaking aims to unlock the full potential of machine learning by harnessing the richness and reliability inherent in FAIR data.

[1] https://www.go-fair.org/fair-principles/

[2] https://gitlab.com/metacook/vocpopuli

Publications

Fabian Schenkel, Benjamin Wohnhas, Wolfgang Gross, Simon Schreiner, Ilia Bagov, and Wolfgang Middelmann "Ship detection and classification with terrestrial hyperspectral data based on convolutional neural networks", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550K (7 October 2019); https://doi.org/10.1117/12.2533090