Tomas Mikolov is a Czech computer scientist best known as the lead creator of word2vec, the technique that turned words into dense numerical vectors and made word embeddings a practical foundation for natural language processing. He earned his Ph.D. at Brno University of Technology for work on recurrent-neural-network language models.
After his doctorate Mikolov joined Google, where in 2013 he led the team that published word2vec, showing that a simple, efficient model could learn word representations capturing striking analogical structure (the canonical example being that the vector for “king” minus “man” plus “woman” lands near “queen”). The follow-up paper, “Distributed Representations of Words and Phrases and their Compositionality,” has accumulated tens of thousands of citations and earned a NeurIPS Test of Time Award. He moved to Facebook AI Research in 2014, where he co-authored fastText, a fast library for text classification and embeddings.
Since 2020 Mikolov has been a senior research scientist at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) in Prague, where he leads a basic-AI-research group working on language models and complex systems.