GUANG LI Guang Li

Photo by Kin 🙂

Short Biography: Guang Li received the B.S. and B.A. degrees with honors in Software Engineering (Japanese Intensive) from Dalian University of Technology, China, in 2019 and the M.S. degree from the Graduate School of Information Science and Technology, Hokkaido University, Japan, in 2022. He is currently pursuing his Ph.D. degree from the Graduate School of Information Science and Technology, Hokkaido University, Japan. He is a student member of AAAI, IEEE, IEICE, and CCF. His research interests include machine learning, data-centric AI, self-supervised learning, and medical image analysis.

He has published more than 10 papers in peer-reviewed journals and conferences as the first/corresponding author. He received the Best Student Paper Award for IEEE GCCE 2020. His papers have been covered by Scientific American, Nikkei, Synced (China), AI Era (China), Deccan Herald (India), and Analytics India Magazine. His papers have been cited at top conferences (NeurIPS, ICML, ICLR, and CVPR) and by researchers at MIT, UC Berkeley, CMU, and Princeton. He has served as a reviewer for prestigious journals such as IEEE Transactions on Medical Imaging, Pattern Recognition, and Neural Networks.

E-mail: guang[at]lmd.ist.hokudai.ac.jp WeChat: abc853215206

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Biography Publication Award Invited Talk Media Coverage Funding Service

News: My friend Dr. Renhe Jiang (Assistant Professor of UTokyo) and I are planning to create a community for Chinese AI Researchers in Japan to share research results and academic information. If you are interested in joining, please do not hesitate to contact us!

 

Biography

Education

  • 2022/04 ~ Present Ph.D. Candidate in Information Science, Hokkaido University
  • 2020/04 ~ 2022/03 M.S. in Information Science, Hokkaido University
  • 2015/09 ~ 2019/06 B.A. in Japanese (Dual Degree), Dalian University of Technology
  • 2015/09 ~ 2019/06 B.S. in Software Engineering, Dalian University of Technology

Experience

  • 2022/04 ~ Present Research Fellow, Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research
  • 2022/07 ~ Present Research Assistant, MEXT Doctoral Program for Data-Related Innovation Expert
  • 2022/06 ~ Present Research Collaborator, Japan Radio Company
  • 2020/06 ~ Present Research Collaborator, Olympus Corporation
  • 2019/07 ~ 2019/09 Research Intern, CASIA Brainnetome Center (Advisor: Prof. Shan Yu)
  • 2018/05 ~ 2019/06 Research Intern, DUT Media Lab (Advisor: Prof. Haojie Li)

 

Publication

Project

  1. Guang Li, Bo Zhao, Tongzhou Wang, “Awesome-Dataset-Distillation,” 2022/08/18. (560 Stars and 23K Visitors, Most Popular AI Research Aug 2022) (Covered by LibHunt, Synced, etc.; Acknowledged by TPAMI 2023) [Dataset Distillation] [Awesome-Dataset-Distillation]

Journal

  1. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Takahiro Ogawa, Miki Haseyama, “Artificial intelligence in the diagnosis of Helicobacter pylori infection using endoscopic images: a multicenter, diagnostic study,” 2023.
  2. Ren Togo*, Guang Li*, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Takahiro Ogawa, Miki Haseyama, “Construction and multicenter diagnostic verification of deep learning system for gastric cancer risk detection from endoscopic images,” 2023. (* Equal Contribution)
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “RGMIM: Region-guided masked image modeling for COVID-19 detection,” 2023. [arXiv]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling,” 2023. [arXiv]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised learning for gastritis detection with gastric X-ray images,” 2023. [arXiv]
  6. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on self-supervised transfer learning using chest X-ray images,” International Journal of Computer Assisted Radiology and Surgery, pp. 1-8, 2022. [arXiv] [Paper]
  7. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Compressed gastric image generation based on soft-label dataset distillation for medical data sharing,” Computer Methods and Programs in Biomedicine, vol. 227, 107189, 2022. [arXiv] [Paper]
  8. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum,” Multimedia Tools and Applications, vol. 81, no. 22, pp. 32287-32303, 2022. [arXiv] [Paper]

International Conference

  1. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Zero-shot traffic sign recognition based on mid-level feature matching,” 2023.
  2. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation using parameter pruning,” 2023. [arXiv]
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” AAAI Conference on Artificial Intelligence (AAAI), Workshop, pp. 1-6, Washington, DC, USA, 2023. [arXiv] [Link]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-knowledge distillation based self-supervised learning for COVID-19 detection from chest X-ray images,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1371-1375, Singapore, 2022. [arXiv] [Paper] [Link]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “TriBYOL: Triplet BYOL for self-supervised representation learning,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3458-3462, Singapore, 2022. (Top 10 Self-supervised Learning Models in 2022) (Covered by Analytics India Magazine, AI Era, etc.) [arXiv] [Paper] [Link]
  6. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised transfer learning for COVID-19 detection from chest X-ray images,” AAAI Conference on Artificial Intelligence (AAAI), Workshop, pp. 1-6, Vancouver, BC, Canada, 2022. [Link]
  7. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Triplet self-supervised learning for gastritis detection with scarce annotations,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 787-788, Kyoto, Japan, 2021. [Paper]
  8. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Cross-view self-supervised learning via momentum statistics in batch normalization,” IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1-2, Penghu, Taiwan, 2021. [Paper]
  9. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Complexity evaluation of medical image data for classification problem based on spectral clustering,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 667-669, Kobe, Japan, 2020. (Best Student Paper Award) [Paper]
  10. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Soft-label anonymous gastric X-ray image distillation,” IEEE International Conference on Image Processing (ICIP), pp. 305-309, Abu Dhabi, UAE, 2020. (The first paper to explore medical dataset distillation; High Attention Score Paper) (Covered by Scientific American, Nikkei, Synced, etc.; Cited by ICLR 2021 Oral (The Second-Highest Rated Paper), CVPR 2022 Oral (CMU, MIT, UC Berkeley), ICML 2022 Oral (Outstanding Paper Award), NeurIPS 2022 (Outstanding Paper Award Nomination), TPAMI 2023, etc.) [arXiv] [Paper] [Link]

Domestic Conference

  1. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A note on traffic sign recognition based on vision transformer adapter using visual feature matching,” ITE Technical Report, Sapporo, 2023.
  2. 李 広, 藤後 廉, 小川 貴弘, 長谷山 美紀, “医療データを対象としたデータセット蒸留に関する検討,” 第1回 北海道大学医療AIシンポジウム, p. 1, 札幌, 2022. (Outstanding Research Award)
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on masked image modeling using vision transformer,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-4, Himeji, 2022.
  4. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on self-supervised learning and self-knowledge distillation,” ITE Technical Report, vol. 46, no. 6, pp. 49-52, Sapporo, 2022.
  5. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Hikari Shimizu, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on EfficientNet with flooding loss,” ITE Technical Report, vol. 45, no. 4, pp. 23-26, Sapporo, 2021.
  6. 李 広, 藤後 廉, 小川 貴弘, 長谷山 美紀, “nAULSに基づくデータセットの複雑性評価に関する検討,” 電気・情報関係学会北海道支部連合大会 講演論文集, pp. 98-99, 札幌, 2020.

 

Award

  1. Outstanding Research Award (Guang Li et al., The First Hokkaido University Medical AI Symposium, 2022/11) [Link]
  2. IEICE Hokkaido Section Student Encouragement Award (2022/03) [Link]
  3. JGC-S Scholarship (2021/09) [Link]
  4. IEEE GCCE 2020 Excellent Student Paper Award Gold Prize (Guang Li et al., GCCE 2020, 2020/10) [Link]
  5. Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research (Ph.D. Fellowship (Two students per year, The only international student), 2020/08) [Link]
  6. Outstanding Graduate of Dalian University of Technology (2019/06)

 

Invited Talk

  1. “AIに基づく異分野融合研究の最新動向,” 岡山大学Society5.0シンポジウム, 小川 貴弘, 2023/02/14. (Guang Li et al., ICIP 2020; ICASSP 2022; Awesome-Dataset-Distillation; CMPB 2022) [Link]
  2. “AI医療を加速するデータマイニングに関する研究–医用画像を対象とした自己教師あり学習技術–,” 日立製作所 中央研究所 北大・日立協働教育研究支援プログラム意見交換会, 李 広, 2022/11/15. [Link]
  3. “AI技術の最新動向と医療分野における応用事例,” 第1回 北海道大学医療AIシンポジウム, 藤後 廉, 2022/11/05. (Guang Li et al., ICIP 2020; ICASSP 2022; Awesome-Dataset-Distillation; CMPB 2022) [Link]
  4. “異分野連携によるデータ駆動型の AI 研究に関する取り組み,” 第129回 日本画像学会年次大会, 小川 貴弘, 2022/06/24. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  5. “AIの社会実装を加速するデータ駆動型研究と人材育成,” Clinical AI Human Resources Development Program 2nd アニュアルシンポジウム, 長谷山 美紀, 2022/02/15. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  6. “マルチメディアAI技術に基づく異分野融合研究と実社会応用,” 映像情報メディア学会 メディア工学研究会, 小川 貴弘, 2021/06/04. (Guang Li et al., ICIP 2020) [Link]
  7. “AIを中心とした医療デジタル技術基盤の構築へ向けた取り組み,” 第60回日本消化器がん検診学会総会 附置研究会3, 藤後 廉, 2021/06/04. (Guang Li et al., ICIP 2020) [Link]

 

Media Coverage

  1. “北大・日立協働教育研究支援プログラム発表会を実施しました,” 北海道大学 大学院総合サイト ニュース, 2023/01/06. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  2. “「飽くなき研究・制作熱意」を大切にするHitachiイズムに震える~北大・日立協働教育研究支援プログラム発表会,” 北海道大学 大学院教育推進機構 レポート, 2023/01/06. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  3. “2022 Top10自监督学习模型总结,” 极市平台, 2022/12/06. (Guang Li et al., ICASSP 2022) [Link]
  4. “第1回 北海道大学医療AIシンポジウムを開催しました,” 北海道大学 大学院医学研究院 医療AI開発者養成プログラム ニュース, 2022/11/25. (Guang Li et al., 第1回 北海道大学医療AIシンポジウム) [Link]
  5. “第1回 北海道大学医療AIシンポジウムを開催しました,” 北海道大学病院 医療AI研究開発センター ニュース, 2022/11/25. (Guang Li et al., 第1回 北海道大学医療AIシンポジウム) [Link]
  6. “2022 Top10自监督学习模型发布!美中两国8项成果霸榜,” 新智元, 2022/11/12. (Guang Li et al., ICASSP 2022) [新智元] [腾讯新闻] [网易新闻] [搜狐新闻]
  7. “Top 10 Self-supervised Learning Models in 2022,” Analytics India Magazine, 2022/11/02. (Guang Li et al., ICASSP 2022) [Link]
  8. “一个项目帮你了解数据集蒸馏Dataset Distillation,” 极市平台, 2022/10/09. (Guang Li et al., ICIP 2020; Awesome-Dataset-Distillation) [Link]
  9. “一个项目帮你了解数据集蒸馏Dataset Distillation,” 机器之心, 2022/10/09. (Guang Li et al., ICIP 2020; Awesome-Dataset-Distillation) [机器之心] [腾讯新闻] [网易新闻] [搜狐新闻]
  10. “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. (Guang Li et al., Awesome-Dataset-Distillation) [Link]
  11. “最先端マルチメディア技術とその実社会応用,” 北海道総合通信局 公式チャンネル, 2022/04/20. (Guang Li et al., ICIP 2020; ICASSP 2022) [Link]
  12. “北海道大学が博士課程向け機構、研究費やキャリア支援,” 日本経済新聞, 2022/04/14. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  13. “肺X線画像からのCOVID-19肺炎検出AIの構築,” 北海道大学病院 医療AI研究開発センター プロジェクト, 2022/03/29. (Guang Li et al., ICASSP 2022) [Link]
  14. “医用画像データ共有へ向けたデータ圧縮技術の構築,” 北海道大学病院 医療AI研究開発センター プロジェクト, 2022/03/29. (Guang Li et al., ICIP 2020) [Link]
  15. “メディア工学の研究動向,” 映像情報メディア年報2021-22シリーズ, 2022/01/01. (Guang Li et al., ICIP 2020) [Link]
  16. “北海道大 情報科学研究院 AI活用でインフラ点検,” 日本経済新聞, 2021/06/16. (Guang Li et al., ICIP 2020) [Link]
  17. “日立による北大博士課程学生に向けた給付型奨学金制度導入,” 科学技術振興機構 産学官連携ジャーナル, 2021/01/15. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  18. “如何让人工智能技术更亲民,” 环球科学, 2021/01/08. (Guang Li et al., ICIP 2020) [Link]
  19. “High Attention Score Paper,” Altmetric, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]
  20. “How to Make Artificial Intelligence More Democratic,” Deccan Herald, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]
  21. “How to Make Artificial Intelligence More Democratic,” Scientific American, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]
  22. “北海道大と日立、博士課程の学生に年520万円の奨学金,” 日本経済新聞, 2020/02/04. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  23. “日立と北大が、博士課程学生に対する研究支援「北大・日立協働教育研究支援プログラム」を開始することに合意,” 北海道大学 プレスリリース, 2020/02/04. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]
  24. “日立と北大が、博士課程学生に対する研究支援「北大・日立協働教育研究支援プログラム」を開始することに合意,” 日立製作所 ニュースリリース, 2020/02/04. (Guang Li et al., 北大・日立協働教育研究支援プログラム) [Link]

 

Funding

  1. Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research, “A Study on Data Mining to Accelerate AI Medicine” (2022/04 ~ 2025/03) [8,400,000 JPY]

 

Service

Journal Reviewer

  1. IEEE Transactions on Medical Imaging
  2. Pattern Recognition
  3. Neural Networks
  4. Information Sciences
  5. Applied Soft Computing
  6. Computers in Biology and Medicine
  7. Digital Signal Processing

Conference Program Committee Member/Reviewer

  1. The AAAI 2023 Workshop on Representation Learning for Responsible Human-Centric AI
  2. The 25th Meeting on Image Recognition and Understanding

 

Misc

ECCV 2022

Given a talk at Hitachi Central Research Laboratory

 

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