PeopleofSuperintelligence

Researcher

Fucong Dai

Fucong Dai is a DeepSeek research and engineering contributor whose work spans language models and kernel software. DeepSeek credited him in its Research & Engineering group for V3 in 2024, and he coauthored DeepSeek-R1 and V3.2 research in 2025. He coauthored the DeepSeek-V4 report and TileKernels, a TileLang kernel library, in 2026. As of October 2026, he is part of DeepSeek-AI's Research & Engineering group and coauthors DeepSeek-V4.1-Flash.

Timeline

  1. 2024

    DeepSeek-V3 research credits

    DeepSeek credits him in its Research & Engineering group for the 2024 V3 technical report. It presents V3 as a mixture-of-experts model pre-trained on 14.8 trillion diverse tokens.

  2. 2025

    DeepSeek-R1 reasoning

    In 2025, he coauthors the DeepSeek-R1 paper published in Nature. It shows that pure reinforcement learning can incentivize language-model reasoning without human-labelled reasoning trajectories.

  3. 2025

    DeepSeek-V3.2 sparse attention

    He coauthors the DeepSeek-V3.2 report in 2025. It introduces DeepSeek Sparse Attention, an efficient mechanism that lowers computational complexity while preserving performance in long-context scenarios.

  4. 2026

    DeepSeek-V4 long context

    In 2026, he coauthors the DeepSeek-V4 report, whose preview models support contexts up to one million tokens. The report presents hybrid attention for long-context efficiency.

  5. 2026

    TileKernels kernel library

    In 2026, he coauthors TileKernels, a kernel library written in TileLang.

  6. As of October 2026

    Research and engineering at DeepSeek

    As of October 2026, he is part of DeepSeek-AI's Research & Engineering group and coauthors DeepSeek-V4.1-Flash. The model is a multimodal mixture-of-experts system for contexts up to one million tokens, with research focused on KV-cache compression.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. DeepSeek-V3 Technical Report arxiv.org. Checked 19 October 2026.
  2. DeepSeek-V3 Technical Report arxiv.org. Checked 19 October 2026.
  3. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature nature.com. Checked 19 October 2026.
  4. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 19 October 2026.
  5. DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 19 October 2026.
  6. GitHub - deepseek-ai/TileKernels: A kernel library written in tilelang github.com. Checked 19 October 2026.
  7. DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arxiv.org. Checked 19 October 2026.