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DATAMIN Research Group

The DATAMIN Research Group within the Babes-Bolyai University, lead by Prof. Csato Lehel, Ph.D., focuses on issues related to processing of large corpus of text documents, Semi-supervised learning, Feature extraction processing methods using Stochastic processes, Robotics.

The Natural Language Processing Group

The Natural Language Processing Group, lead by Prof. Dan Cristea, Ph.D., includes post-doc researchers but also PhD, master and undergraduate students. The Group is more stable at the top and more fluctuating at the base. As undergraduate students get an interest on NLP research, after producing a graduation paper in this domain, they usually enrol…

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Speech and Dialogue Laboratory

Speech and Dialogue (SpeeD) Laboratory is a teaching and research laboratory within University “Politehnica” of Bucharest. SpeeD Lab was founded in 1984 by Professor Corneliu Burileanu, who is also the leader of the laboratory ever since. SpeeD Lab’s staff is currently composed of 4 full-time academic members (2 full professors and 2 lecturers) and several PhD and master students.

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The Center for Computational Linguistics

The Center of Computational Linguistics, lead by Prof. Liviu Dinu, Ph.D. and Prof. Emil Ionescu, Ph.D., is a multi-disciplinary research area that addresses the automatic processing of human language for a range of tasks. We are a high-level research center inside the University of Bucharest, with our members having gained experience (as graduates, PhD, master students, or full employees) from the Faculty of Letters, the Faculty of Foreign Languages and Literatures, and the Faculty of Mathematics and Computer Science.

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Knowledge Engineering Group

The Knowledge Engineering Group (KEG), lead by Prof. Rodica Potolea, PhD, is addressing problems for knowledge extraction, representation, storage and management that the information era has brought in various segments of human activity due to data overload.

The main fundamental theoretical aspects our group focuses on are: dealing with problem-specific features extraction from both structured data, pre-processing techniques for handling noisy and/or incomplete data, learning from balanced/unbalanced and structured/unstructured data.

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