PeopleofSuperintelligence

Researcher

Abdelrahman Mohamed

Also known as A. Shereef Naina Mohamed, Abdel-rahman Mohamed

Abdelrahman Mohamed is a speech and machine-learning researcher whose work helped extend deep learning into speech recognition. He earned a PhD at the University of Toronto with Gerald Penn and Geoffrey Hinton, then worked at Microsoft Research, Amazon Alexa AI, and Facebook AI Research. His coauthored wav2vec 2.0 and HuBERT systems learn speech representations from audio, and he received the 2016 IEEE Signal Processing Society Best Journal Paper Award. As of October 2026, he is affiliated with FAIR at Meta Superintelligence Labs; from 2022 to 2025, he served as Rembrand's Chief Science Officer.

Works
178
Citations
48,426
h-index
39
Badges

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

  1. 2009

    Deep learning for spoken language

    His University of Toronto PhD work with Gerald Penn and Geoffrey Hinton puts him on the team that starts deep-learning research in spoken-language processing in 2009.

  2. 2012

    Deep neural networks for speech

    With George Dahl and Geoffrey Hinton, he coauthors a 2012 overview of four research groups' work on deep neural networks for acoustic modeling in speech recognition. It reports that these networks outperform Gaussian mixture models on speech-recognition benchmarks.

  3. 2014–2018

    Research roles across three labs

    From 2014 to 2017, he is a researcher at Microsoft Research in Redmond. Before joining FAIR in September 2018, he is a principal scientist manager at Amazon Alexa AI.

  4. 2016

    An IEEE paper award

    In 2016, he receives the IEEE Signal Processing Society Best Journal Paper Award.

  5. 2020

    wav2vec 2.0 for speech

    In 2020, he coauthors wav2vec 2.0, a framework that learns speech representations from audio alone and improves recognition after fine-tuning on transcribed speech.

  6. 2021

    HuBERT learns from masked speech

    In 2021, he coauthors HuBERT, a method that learns speech representations with offline clustering and a prediction loss over masked audio segments.

  7. 2022–2025

    Co-founding Rembrand

    At Rembrand, he is a co-founder and serves as Chief Science Officer from 2022 to 2025. The company builds its approach around viewers' wish to watch content without interruption.

  8. As of October 2026

    FAIR at Meta Superintelligence Labs

    As of October 2026, he is affiliated with FAIR at Meta Superintelligence Labs. In September 2026, he coauthors RetroThinker, a framework that lets speech language models revise their reasoning during inference.

A language model matched the links in this timeline to people, organizations and papers (provisional, model-judged).

Career

  1. NowFAIR (Meta)
  2. 2023–2026Tuskegee University
  3. 2022–2025RembrandCo-founder and Chief Science Officer
  4. 2018–2023Meta
  5. 2017–2018AmazonPrincipal Scientist Manager
  6. 2017–2025Cairo University
  7. 2015–2017Microsoft
  8. 2014–2017Microsoft ResearchResearcher

Education

  1. University of TorontoPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups2012 · IEEE Signal Processing Magazine · 10,162 citationsWith Andrew W. Senior, Dong Yu, Geoffrey Hinton, Li Deng, Tara Sainath and Vincent Vanhoucke
  2. Speech recognition with deep recurrent neural networks2013 · IEEE International Conference on Acoustics Speech and Signal Processing · 8,880 citationsWith Alex Graves and Geoffrey Hinton
  3. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension2020 · Annual Meeting of the Association for Computational Linguistics (ACL) · 8,786 citationsWith Luke Zettlemoyer and Omer Levy
  4. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units2021 · IEEE/ACM Transactions on Audio Speech and Language Processing · 2,948 citationsWith Ruslan Salakhutdinov
  5. Convolutional Neural Networks for Speech Recognition2014 · IEEE/ACM Transactions on Audio Speech and Language Processing · 2,265 citationsWith Dong Yu and Li Deng

From OpenAlex, as of 18 October 2026.

  1. Google alumni founded or led 77 other organizations, the most among the labs we compareIncludes Abdelrahman Mohamed. We trace 177 organizations founded or led by AI lab alumni, and 186 career moves between the labs.
  2. 13% of AI career moves in the 2020s went from a university to a companyIncludes Abdelrahman Mohamed. Our estimates of how university-to-company career transitions changed by decade, including roles added alongside an earlier job.
  3. The company share of AI researchers’ top papers rose from 2% to 33%Includes Meta. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 2% in 1975–1979 to 33% in 2020–2024.
  4. 44% of AI career moves go from one university to anotherIncludes Meta. We estimate how 11,906 AI career moves link universities, companies, labs and government research centers, with net flows and the most common paths.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Abdelrahman Mohamed cs.toronto.edu. Checked 18 October 2026.
  2. Deep Neural Networks for Acoustic Modeling in Speech Recognition research.google. Checked 18 October 2026.
  3. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations arxiv.org. Checked 18 October 2026.
  4. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units arxiv.org. Checked 18 October 2026.
  5. About Us New rembrand.com. Checked 18 October 2026.
  6. RetroThinker: Enabling Retrospective Thinking in Speech LLMs arxiv.org. Checked 18 October 2026.