Eugenia Oshurko

Simulation Neuroscience
Knowledge Engineering team


Eugenia Oshurko is a Knowledge Engineer in the Data and Knowledge Engineering team in the Simulation Neuroscience Division.


In her role, she focuses on neuroscience knowledge graphs, building knowledge representations and developing frameworks and services for graph analytics.

Eugenia has a PhD in Computer Science from the École Normale Superieure de Lyon (ENS Lyon), France where her research focus was graph-based knowledge representation and its application to computational biology. She gained her Master’s Degree in Computer Science (special training in modelling complex systems) also from ENS Lyon, having won the ENS Lyon Ampere Excellence Scholarship. Prior to these, she studied for a Bachelor Degree in Software Engineering from Kyiv-Mohyla Academy, Ukraine.

In her free time, Eugenia enjoys cooking, drawing, painting, and going hiking.



Schema Validation and Evolution for Graph Databases, Angela Bonifati, Peter Furniss, Alastair Green, Russ Harmer, Eugenia Oshurko, Hannes Voigt. Conceptual Modeling: 38th International Conference, ER 2019 (2019).

Knowledge representation and update in hierarchies of graphs, Russ Harmer and Eugenia Oshurko. Journal of Logical and Algebraic Methods in Programming (2020).

Bio-curation for cellular signalling: the KAMI project, Russ Harmer, Yves-Stan Le Cornec, Sébastien Légaré, Eugenia Oshurko. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019).

KAMIStudio: An Environment for Biocuration of Cellular Signalling Knowledge, Russ Harmer, Eugenia Oshurko. International Conference on Computational Methods in Systems Biology (2019).

Reversibility and composition of rewriting in hierarchies, Russ Harmer, Eugenia Oshurko. Electronic Proceedings in Theoretical Computer Science (2020).