Imperial College London

Dr Ovidiu Șerban

Faculty of EngineeringDepartment of Computing

Research Fellow in Intelligent Data Processing and Curation



o.serban Website




DSI Main OfficeWilliam Penney LaboratorySouth Kensington Campus





Ovidiu Șerban (ʃerban) is a Research Fellow at the Data Science Institute (DSI), Imperial College London. His current work includes real-time Natural Language Processing, Data Curation and Large Scale Visualisation Systems.

Ovidiu’s research topics are Natural Language Processing, Machine Learning, Affective Computing and Interactive System Design. He holds a joint PhD from INSA de Rouen Normandy (France) and “Babeș-Bolyai” University (Romania), while working at LITIS Laboratory in France.

In his youth, Ovidiu worked at the Institute for Security Science and Technology (ISST), Imperial College London; Computer Lab, University of Cambridge, UK and ISR Laboratory, University of Reading, UK.



Hilman D, Serban O, 2022, A unified Link Prediction architecture applied on a novel heterogenous Knowledge Base, Knowledge-based Systems, Vol:241, ISSN:0950-7051, Pages:1-17

Vaghela U, Rabinowicz S, Bratsos P, et al., 2021, Using a secure, continually updating, web source processing pipeline to support the real-time data synthesis and analysis of scientific literature: development and validation study, Journal of Medical Internet Research, Vol:23, ISSN:1438-8871, Pages:1-14

Fernando S, Amador Díaz López J, Şerban O, et al., 2020, Towards a large-scale twitter observatory for political events, Future Generation Computer Systems, Vol:110, ISSN:0167-739X, Pages:976-983

Fernando S, Scott-Brown J, Şerban O, et al., 2020, Open Visualization Environment (OVE): A web framework for scalable rendering of data visualizations, Future Generation Computer Systems, Vol:112, ISSN:0167-739X, Pages:785-799

Hankin CL, Serban O, Thapen N, et al., 2019, Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification, Information Processing and Management, Vol:56, ISSN:0306-4573, Pages:1166-1184

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