GUANG LI Guang Li

Short Biography: Guang Li is a Specially Appointed Assistant Professor with the Education and Research Center for Mathematical and Data Science, Hokkaido University. He received the B.S. and B.A. degrees in Software Engineering (Japanese Intensive) from Dalian University of Technology, China, in 2019, and the M.S. and Ph.D. degrees in Information Science from Hokkaido University, Japan, in 2022 and 2023, respectively. His research interests include Machine Learning, Data-Centric AI, Self-Supervised Learning, and Medical Image Analysis.

He has published over 20 first-authored papers in journals and conferences, including CBM, CMPB, MTAP, IJCARS, ICASSP, and ICIP. He received the Best Student Paper Award for IEEE GCCE 2020. His papers have been covered by prestigious media such as Scientific American, Nikkei, Synced, AI Era, Deccan Herald, and Analytics India Magazine. He has served as a reviewer for over 15 journals and conferences, including NN, PR, AAAI, MICCAI, and ICASSP. He is a member of AAAI, IEEE, and IEICE.

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

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Biography Publication Honors and Awards Funding Media Coverage Academic Service

Biography

  • 2023/10 ~ Present Hokkaido University, MDS Center, Specially Appointed Assistant Professor
  • 2022/04 ~ 2023/09 Hokkaido University-Hitachi Joint Cooperative Program, Research Fellow
  • 2022/04 ~ 2023/09 Hokkaido University, Ph.D. in Information Science
  • 2020/04 ~ 2022/03 Hokkaido University, M.S. in Information Science
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.A. in Japanese
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.S. in Software Engineering

 

Publication

Project

  1. Guang Li, Bo Zhao, Tongzhou Wang, “Awesome-Dataset-Distillation,” 2022/08/07. (860 Stars; Most Popular AI Research Aug 2022) (Covered by LibHunt, Synced, etc.) [Dataset Distillation] [Awesome-Dataset-Distillation] [Survey]

Journal

  1. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Importance-aware adaptive dataset distillation,” Neural Networks, 2023. (Under revision)
  2. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “RGMIM: Region-guided masked image modeling for learning meaningful representation from X-ray images,” International Journal of Imaging Systems and Technology, 2023. [arXiv] (Under review)
  3. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation using parameter pruning,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E107-A, no. 6, 2023. [arXiv] [Paper]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised learning for gastritis detection with gastric X-ray images,” International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 10, pp. 1841-1848, 2023. [arXiv] [Paper]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling,” Computers in Biology and Medicine, vol. 158, 106877, 2023. [arXiv] [Paper]
  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, vol. 18, no. 4, pp. 715-722, 2023. [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. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” AAAI 2023 Workshop on Representation Learning for Responsible Human-Centric AI, pp. 1-6, Washington, DC, USA, 2023. [arXiv] [Link]
  2. 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]
  3. 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]
  4. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised transfer learning for COVID-19 detection from chest X-ray images,” AAAI 2022 Workshop on Human-Centric Self-Supervised Learning, pp. 1-6, Vancouver, BC, Canada, 2022. [Link]
  5. 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]
  6. 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]
  7. 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]
  8. 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 and privacy preservation; Top 3% Attention Score Paper) (Covered by Scientific American, Nikkei, Synced, Altmetric, etc.) [arXiv] [Paper] [Link]

Domestic Conference

  1. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation via self-adaptive parameter matching,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Hamamatsu, 2023. [Link]
  2. 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, vol. 47, no. 6, pp. 208-211, Sapporo, 2023.
  3. 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) [Link]
  4. 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. [Link]
  5. 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.
  6. 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.
  7. 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.

 

Honors and Awards

  1. The Dean’s Award of Graduate School of Information Science and Technology (2023/09) [Link]
  2. The First Hokkaido University Medical AI Symposium Outstanding Research Award (2022/11) [Link]
  3. IEICE Hokkaido Section Student Encouragement Award (2022/03) [Link]
  4. IEEE GCCE 2020 Excellent Student Paper Award Gold Prize (2020/10) [Link]
  5. Outstanding Graduate of Dalian University of Technology (2019/06)

 

Funding

  1. Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research, “A Study on Data Mining to Accelerate AI Medicine,” 2022/04 ~ 2023/09. (Principal Investigator)

 

Media Coverage

  1. “CO2吸収量、森林の9倍 えりものコンブ 町の調査報告,” 北海道新聞, 2023/09/27. (Guang Li et al., ブルーカーボン研究) [Link]
  2. “情報科学院学位記授与式及び学院長賞授与式挙行,” 北海道大学 大学院情報科学院 ニュース, 2023/09/27. (Guang Li et al., 学院長賞) [Link]
  3. “第1回北海道大学医療AIシンポジウム開催報告,” 北海道放射線医学雑誌, 2023/03/23. (Guang Li et al., 第1回 北海道大学医療AIシンポジウム 優秀研究賞) [Link]
  4. “浓缩就是精华：用大一统视角看待数据集蒸馏,” CVer, 2023/01/09. (Guang Li et al., Awesome-Dataset-Distillation) [Link]
  5. “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]
  6. “「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]
  7. “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]
  8. “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 優秀研究賞) [Link]
  9. “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 優秀研究賞) [Link]
  10. “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) [Link]
  11. “Top 10 Self-supervised Learning Models in 2022,” Analytics India Magazine, 2022/11/02. (Guang Li et al., ICASSP 2022) [Link]
  12. “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]
  13. “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) [Link]
  14. “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. (Guang Li et al., Awesome-Dataset-Distillation) [Link]
  15. “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]
  16. “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]
  17. “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]
  18. “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]
  19. “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]
  20. “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]
  21. “Top 3% Attention Score Paper,” Altmetric, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]
  22. “How to Make Artificial Intelligence More Democratic,” Deccan Herald, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]
  23. “How to Make Artificial Intelligence More Democratic,” Scientific American, 2021/01/02. (Guang Li et al., ICIP 2020) [Link]

 

Academic Service

Journal Reviewer

  1. Neural Networks
  2. Pattern Recognition
  3. Knowledge-Based Systems
  4. Expert Systems with Applications
  5. Artificial Intelligence in Medicine
  6. Computer Vision and Image Understanding
  7. Information Sciences
  8. Applied Soft Computing
  9. Neurocomputing
  10. Biomedical Signal Processing and Control
  11. Digital Signal Processing

Conference Program Committee Member/Reviewer

  1. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)
  2. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023)
  3. The AAAI 2023 Workshop on Representation Learning for Responsible Human-Centric AI
  4. Meeting on Image Recognition and Understanding (MIRU 2023)
  5. Meeting on Image Recognition and Understanding (MIRU 2022)

Misc

AAAI 2022 @Vancouver

AAAI 2023 @Washionton D.C.

Given a talk at the Hitachi Central Research Laboratory

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