PeopleofSuperintelligence

Researcher

Haowei Zhang

Haowei Zhang is a researcher whose DeepSeek work spans open language models, mathematical reasoning, vision-language models and reinforcement learning. He coauthored DeepSeek LLM, DeepSeekMath and DeepSeek-V2 in 2024, as well as DeepSeek-VL2 and DeepSeek-V3 in December 2024. Nature published the DeepSeek-R1 study in September 2025, and he coauthored DeepSeek-V3.2 in December 2025. In April 2026, he coauthored a preview of DeepSeek-V4 with a one-million-token context and was a former member of the DeepSeek team.

Timeline

  1. January–May 2024

    Early DeepSeek models

    In January 2024, he coauthors DeepSeek LLM, a project for open-source language models. In February 2024, he coauthors DeepSeekMath, which introduces a 7B model trained with 120 billion math-related tokens. In May 2024, he coauthors DeepSeek-V2, a mixture-of-experts language model.

  2. December 2024

    Vision-language and V3

    In December 2024, he coauthors DeepSeek-VL2, a mixture-of-experts vision-language model series, and the DeepSeek-V3 technical report, which presents a 671-billion-parameter model.

  3. January–September 2025

    DeepSeek-R1 in Nature

    He coauthors DeepSeek-R1 in January 2025, which explores reasoning through pure reinforcement learning. Nature publishes the study in September 2025.

  4. December 2025

    The V3.2 report

    In December 2025, he coauthors a report on DeepSeek-V3.2, a model that combines computational efficiency with reasoning and agent performance.

  5. April 2026

    DeepSeek-V4 and departure

    In April 2026, he coauthors a preview of DeepSeek-V4, a model series with a one-million-token context. He is a former member of the DeepSeek team by April 2026.

  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 LLM: Scaling Open-Source Language Models with Longtermism arxiv.org. Checked 19 October 2026.
  2. DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models arxiv.org. Checked 19 October 2026.
  3. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model arxiv.org. Checked 19 October 2026.
  4. DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding arxiv.org. Checked 19 October 2026.
  5. DeepSeek-V3 Technical Report arxiv.org. Checked 19 October 2026.
  6. DeepSeek-V3 Technical Report | alphaXiv alphaxiv.org. Checked 19 October 2026.
  7. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning arxiv.org. Checked 19 October 2026.
  8. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning nature.com. Checked 19 October 2026.
  9. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 19 October 2026.
  10. DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 19 October 2026.
  11. DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 19 October 2026.