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Tomáš Mikolov

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Tomáš Mikolov
Mikolov in 2020
Born (1982-10-08) 8 October 1982 (age 43)
Šumperk
CitizenshipCzech
Alma materBrno University of Technology
Scientific career
FieldsComputer Science
InstitutionsJohns Hopkins University
Université de Montréal
Microsoft
Google
Facebook
ThesisStatistical Language Models Based on Neural Networks (2012)

Tomáš Mikolov is a Czech computer scientist known for his work on neural language models and word representations.[1] He was the lead author of the 2013 paper that introduced the word2vec models, a technique for learning word embeddings from text.[2] He later co-authored research associated with fastText.[3]

Mikolov received his PhD from Brno University of Technology and worked at Microsoft Research, Google Brain, and Facebook AI Research. In 2020, he joined the Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague.[4]

Career

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During his doctoral studies at Brno University of Technology, Mikolov spent time at Johns Hopkins University. The visit was arranged with the support of Sanjeev Khudanpur and Frederick Jelinek. He also spent several months in Yoshua Bengio's machine-learning laboratory at the Université de Montréal.[5]

After completing his PhD in 2012, Mikolov joined Google Brain. In 2014, he moved to Facebook AI Research (FAIR), where he worked on natural language processing and more general artificial-intelligence research.[6]

In 2020, Mikolov returned to the Czech Republic and joined the Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague. He headed a new research group focused on mathematical models capable of increasing in complexity.[7]

In 2025, Mikolov co-founded BottleCap AI, a Prague-based company developing efficient foundation models.[8]

Research

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Mikolov's early work focused on applying recurrent neural networks to language modelling. His 2012 doctoral dissertation, Statistical Language Models Based on Neural Networks, examined neural-network approaches to predicting text.[9]

At Google, he led the work that introduced word2vec, a method for learning word embeddings from large text collections.[10] A follow-up paper, Distributed Representations of Words and Phrases and their Compositionality, introduced techniques including negative sampling and later received the 2023 NeurIPS Test of Time Award.[11]

While at Facebook AI Research, Mikolov co-authored work on fastText, including methods for text classification and word representations based on character subwords.[12] He also worked on mapping word representations between languages for use in bilingual dictionaries and statistical machine translation.[13]

References

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  1. ↑ "Industry Leaders in Signal Processing and Machine Learning: Tomas Mikolov". IEEE Signal Processing Society. July 2021. Retrieved 5 August 2026.
  2. ↑ Mikolov, Tomáš; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
  3. ↑ Bojanowski, Piotr; Grave, Édouard; Joulin, Armand; Mikolov, Tomáš (2017). "Enriching Word Vectors with Subword Information". Transactions of the Association for Computational Linguistics. 5: 135–146. doi:10.1162/tacl_a_00051.
  4. ↑ "Internationally acclaimed expert Tomáš Mikolov coming from Facebook AI to join CIIRC CTU". Czech Institute of Informatics, Robotics and Cybernetics. 11 May 2020. Retrieved 5 August 2026.
  5. ↑ Mikolov, Tomáš (2012). Statistical Language Models Based on Neural Networks (PDF) (PhD thesis). Brno University of Technology. Retrieved 6 August 2026.
  6. ↑ "Industry Leaders in Signal Processing and Machine Learning: Tomas Mikolov". IEEE Signal Processing Society. 1 July 2021. Retrieved 6 August 2026.
  7. ↑ "Internationally acclaimed expert Tomáš Mikolov coming from Facebook AI to join CIIRC CTU and focus on developing strong artificial intelligence". Czech Institute of Informatics, Robotics and Cybernetics. 11 May 2020. Retrieved 6 August 2026.
  8. ↑ Leprince-Ringuet, Daphné (29 January 2026). "'Efficiency as a fundamental principle': Google and Meta alumni raise $7.5m to build new approach to LLMs". Sifted. Retrieved 6 August 2026.
  9. ↑ Mikolov, Tomáš (2012). Statistical Language Models Based on Neural Networks (PhD thesis). Brno University of Technology.
  10. ↑ Mikolov, Tomáš; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
  11. ↑ "Announcing the NeurIPS 2023 Paper Awards". NeurIPS Blog. 11 December 2023. Retrieved 6 August 2026.
  12. ↑ Bojanowski, Piotr; Grave, Édouard; Joulin, Armand; Mikolov, Tomáš (2017). "Enriching Word Vectors with Subword Information". Transactions of the Association for Computational Linguistics. 5: 135–146. doi:10.1162/tacl_a_00051.
  13. ↑ Mikolov, Tomáš; Le, Quoc V.; Sutskever, Ilya (2013). "Exploiting Similarities among Languages for Machine Translation". arXiv:1309.4168 [cs.CL].
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