PeopleofSuperintelligence

Researcher

Qiancheng Wang

Also known as 王前程

Qiancheng Wang is a researcher whose work spans supersonic flow and large language models. He coauthored a 2020 review of curved-wall turbulent boundary layers and later studies of shock interaction in a double fin and energy separation in a supersonic vortex tube. DeepSeek credited him on V3, R1 and V3.2, reports about a mixture-of-experts model and reasoning through reinforcement learning. As of October 2026, he contributes to DeepSeek-AI's Research & Engineering group for V4.1-Flash.

Now at
DeepSeek
Works
36
Citations
1,414
h-index
16
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2020

    Curved-wall boundary-layer review

    In 2020, he coauthors with Mingbo Sun, Xu Wang and Yuan Liu a review of supersonic turbulent boundary layers on curved surfaces.

  2. September 2022

    A double-fin shock study

    In September 2022, he coauthors an experimental study of crossing shock waves and a turbulent boundary layer inside a double fin, using micro-ramp vortex generators to suppress flow separation.

  3. December 2024

    DeepSeek-V3 engineering credits

    In December 2024, DeepSeek credits him in its Research & Engineering group for DeepSeek-V3, a Mixture-of-Experts language model with 671 billion total parameters and 37 billion activated for each token.

  4. January 2025

    Supersonic vortex-tube analysis

    In January 2025, he coauthors with Li Le and Zhao Bin an analysis of energy separation in a supersonic vortex tube, published online in volume 21, issue 6 of the journal's 2024 edition. At NUDT's College of Aerospace Science and Engineering, he is an associate researcher focused on high-speed aircraft inlet and exhaust design and hypersonic boundary layers.

  5. September 2025

    DeepSeek-R1 and reasoning

    In September 2025, he coauthors the Nature study “DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning.”

  6. December 2025

    DeepSeek-V3.2 open model

    In December 2025, he coauthors “DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models.”

  7. As of October 2026

    DeepSeek-V4.1-Flash research

    As of October 2026, he contributes to DeepSeek-AI's Research & Engineering group for DeepSeek-V4.1-Flash, whose report appears in September 2026.

Career

  1. NowDeepSeek
  2. 2013–2025National University of Defense TechnologyAssociate Researcher

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

Most-cited works

  1. Can Open Large Language Models Catch Vulnerabilities?2025 · 539 citationsWith Bingxuan Wang, Chengqi Deng, Damai Dai, Erhang Li, Fangyun Lin and Haocheng Wang
  2. DeepSeek-V3 Technical Report2024 · 292 citationsWith Bingxuan Wang, Chengqi Deng, Damai Dai, Daya Guo, Dejian Yang and Erhang Li
  3. On the impact of adverse pressure gradient on the supersonic turbulent boundary layer2016 · Physics of Fluids · 62 citations
  4. An experimental investigation of the supersonic turbulent boundary layer subjected to concave curvature2016 · Physics of Fluids · 55 citations
  5. Wall temperature effects on shock wave/turbulent boundary layer interaction via direct numerical simulation2020 · Acta Astronautica · 50 citations

From OpenAlex, as of 21 October 2026.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Qiancheng Wang. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. 16% of AI career moves in the 2020s went from a university to a companyIncludes Qiancheng Wang. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes DeepSeek. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. 5,000 frontier users would be 38.2% of the core labs' 13,100 reported staffIncludes DeepSeek. 5,000 frontier users would be 38.2% of the core labs' 13,100 reported staff. We test the estimate against 3,913 named model contributors and dated all-staff records across Google DeepMind, OpenAI and Anthropic, plus six additional frontier-grade labs.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Qiancheng Wang yet. Send us a free-licensed photo.

  1. Overview on progress of supersonic turbulent boundary layer on curved surfaces pubs.cstam.org.cn. Checked 21 October 2026.
  2. Three-dimensional characteristics of crossing shock wave turbulent boundary layer interaction in a double fin with and without micro-ramp control faculty.csu.edu.cn. Checked 21 October 2026.
  3. DeepSeek-V3 Technical Report arxiv.org. Checked 21 October 2026.
  4. Analysis of energy separation efficiency of a supersonic vortex tube cslgxbzk.csust.edu.cn. Checked 21 October 2026.
  5. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning nature.com. Checked 21 October 2026.
  6. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 21 October 2026.
  7. DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arxiv.org. Checked 21 October 2026.