Artem Revenko is a research-driven technology leader with 13 years of experience spanning academia and industry, currently leading research projects at Graphwise in Vienna. He has steered R&D teams at Semantic Web Company, designing algorithms for Linked Data, AI, and data/text mining, and combines deep mathematical and physics training with practical software engineering. Artem holds advanced degrees including a PhD-level background from TU Wien/TU Dresden and MSU, which he leverages to bridge theoretical methods and production-ready systems. As an active full‑stack open-source contributor to projects like NiceGUI, he improves developer-facing UI components and testing coverage, showing hands-on commitment to quality and usability. Comfortable across Python and Java ecosystems, he has a track record of turning complex research problems—such as word-sense disambiguation and prognostics—into deployable solutions. Colleagues value him for translating rigorous academic insight into pragmatic engineering and for shipping measurable improvements in both research and product contexts.
13 years of coding experience
Vienna University of Technology
Ph.D, Informatics, Ph.D, Informatics at Technische Universität Dresden
Master's degree, Computational and Applied Mathematics, Master's degree, Computational and Applied Mathematics at Higher School of Economics
Candidate of Science, Mathematics, Candidate of Science, Mathematics at Московский Государственный Университет им. М.В. Ломоносова (МГУ)
Create web-based user interfaces with Python. The nice way.
Role in this project:
Full-stack Developer
Contributions:1 PR, 3 comments, 2 issues in 10 months
Contributions summary:Artem primarily contributed to the NiceGUI framework, focusing on enhancing the `select` element. Their work involved enabling free text input, adding functionality for handling new and unique values, and integrating these features within the testing framework. The commits also included refactoring and merge operations. Furthermore, the user added new testing cases to cover the newly added features of the `select` element, improving code coverage.
Usage of pre-trained language model to induce and disambiguate entity senses
Contributions:67 commits, 3 PRs, 47 pushes in 3 years 7 months
pytorchlanguage-modelsensesdeep-learningentity
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Artem Revenko - Research Projects Team Lead at Graphwise