PeopleofSuperintelligence

Researcher

Gang Niu

Gang Niu is a machine learning researcher at RIKEN's Center for Advanced Intelligence Project in Tokyo, where he works on learning from weak and imperfect labels. He earned a Doctor of Engineering at Tokyo Institute of Technology under Masashi Sugiyama in 2013, then worked as a senior software engineer at Baidu and as an assistant professor at the University of Tokyo before joining RIKEN in 2018. Among his coauthored papers are a non-negative risk estimator for positive-unlabeled learning in 2017 and Co-teaching for noisy labels in 2018. He received the 2018 RIKEN BAIHO Award, and in 2022 PiCO, a paper he coauthored, received an ICLR Outstanding Paper Honorable Mention while his MIT Press book Machine Learning from Weak Supervision appeared. He is a senior research scientist on RIKEN AIP's Imperfect Information Learning Team, an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence since 2024, and a senior area chair for NeurIPS 2026.

Now at
RIKEN
Badges

Timeline

  1. 2007–2013

    Mathematics, then a Tokyo Tech doctorate

    He earns a Bachelor of Science in mathematics from Southeast University in 2007 and a Master of Engineering in computer science and technology from Nanjing University in 2010, supervised by Lin Shang. In 2013, he completes a Doctor of Engineering in computer science at Tokyo Institute of Technology under Masashi Sugiyama.

  2. 2013–2018

    Postdoc, Baidu, then Tokyo faculty

    He is a postdoctoral fellow in the Department of Computer Science at Tokyo Institute of Technology from October 2013 to March 2014. From April 2014 to July 2015, he is a senior software engineer in the Department of Natural Language Processing at Baidu in Beijing. In September 2015, he becomes an assistant professor in the Department of Complexity Science and Engineering at the University of Tokyo, a post he holds until March 2018, and he is a visiting scientist at RIKEN AIP from September 2017.

  3. 2017–2018

    PU learning and Co-teaching

    In 2017, he coauthors a non-negative risk estimator for positive-unlabeled learning with Ryuichi Kiryo, Marthinus du Plessis and Masashi Sugiyama; it is more robust against overfitting, lets deep networks learn from limited positive data, and is selected for oral presentation at NIPS 2017. He also coauthors "Learning from Complementary Labels" in 2017, which trains classifiers from labels that name a class a pattern does not belong to. In 2018, he coauthors Co-teaching, in which two deep networks teach each other to cope with noisy labels.

  4. 2018–2020

    Research scientist at RIKEN AIP

    He joins RIKEN's Center for Advanced Intelligence Project as a research scientist in April 2018. In October 2020, he is tenured.

  5. 2018

    The RIKEN BAIHO Award

    He receives the 2018 RIKEN BAIHO Award from the RIKEN president for his research on weakly supervised classification and robust learning, at a ceremony in May 2019.

  6. 2022

    ICLR honorable mention, MIT Press book

    He coauthors "PiCO: Contrastive Label Disambiguation for Partial Label Learning" with Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Chen and Junbo Zhao, and it receives an Outstanding Paper Honorable Mention at ICLR 2022. In August 2022, MIT Press publishes his coauthored book Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach. He is also a publications chair for ICML 2022.

  7. April 2023

    Promotion to senior research scientist

    RIKEN AIP promotes him to senior research scientist in April 2023, and he is a core member of its Imperfect Information Learning Team, directed by Masashi Sugiyama.

  8. As of October 2026

    Senior scientist, editor, area chair

    He continues as a senior research scientist at RIKEN AIP. He becomes an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence in June 2024 and an action editor of Machine Learning in September 2024. In 2026, he serves as a senior area chair for ICML 2026 and NeurIPS 2026.

Career

  1. Since 2017RIKENVisiting Scientist, Research Scientist
  2. 2015–2018University of TokyoAssistant Professor
  3. 2014–2015BaiduSenior Software Engineer
  4. 2013–2014Institute of Science TokyoPostdoctoral Fellow

Education

  1. 2013Institute of Science TokyoDoctor of Engineering
  2. 2010Nanjing UniversityMaster of Engineering
  3. 2007Southeast UniversityBachelor of Science

From the biography.

  1. 23% of people shaping AI who left a university for a company later returned to a universityIncludes Gang Niu. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes RIKEN. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. The United States employs an estimated 48% of people shaping AIIncludes RIKEN. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes RIKEN. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Colleagues

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

Sources and credits

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

  1. Gang Niu (Senior Research Scientist at RIKEN) niug1984.github.io. Checked 21 October 2026.
  2. [1703.00593] Positive-Unlabeled Learning with Non-Negative Risk Estimator arxiv.org. Checked 21 October 2026.
  3. [1705.07541] Learning from Complementary Labels arxiv.org. Checked 21 October 2026.
  4. [1804.06872] Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels arxiv.org. Checked 21 October 2026.
  5. Research Scientist Gang Niu received the 2018 RIKEN BAIHO Award aip.riken.jp. Checked 21 October 2026.
  6. Outstanding Paper Honorable Mention at ICLR 2022 aip.riken.jp. Checked 21 October 2026.
  7. ICML 2022 Committees icml.cc. Checked 21 October 2026.
  8. Imperfect Information Learning Team | RIKEN riken.jp. Checked 21 October 2026.