Tomáš Mikolov (born October 8, 1982, in Šumperk) is a Czech computer scientist known for his work on neural language models and word representations.^[16] He completed his PhD at the Brno University of Technology in 2012 with a thesis titled "Statistical Language Models Based on Neural Networks".

Mikolov was the lead author of the 2013 paper that introduced the word2vec models, a technique for learning word embeddings from text, and he later co-authored research associated with fastText.^[17,18] His career includes affiliations with Johns Hopkins University, Université de Montréal, 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.^[19]

During his doctoral studies at Brno University of Technology, Mikolov spent time at Johns Hopkins University, a visit 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.^[19] 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.^[20]

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, where he headed a new research group focused on mathematical models capable of increasing in complexity.^[21] In 2025, Mikolov co-founded BottleCap AI, a Prague-based company developing efficient foundation models.^[16]

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.^[22]

At Google, he led the work that introduced word2vec, a method for learning word embeddings from large text collections.^[17] 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.^[23]

While at Facebook AI Research, Mikolov co-authored work on fastText, including methods for text classification and word representations based on character subwords.^[18] He also worked on mapping word representations between languages for use in bilingual dictionaries and statistical machine translation.^[24]