Shintaro Fukushima
Ph.D. (Information Science and Technology)
E-mail: sfukushim [at] gmail.com
[Google Scholar] [Researchmap] [ResearchGate] [ORCID] [DBLP] [Amazon] [LinkedIn] [Facebook]

Affiliation

• Principal Researcher/Project General Manager, InfoTech, Digital Information Communication Dep., Toyota Motor Corporation. [website] [profile] [interview]

• Project Associate Professor, Data Science and AI Innovation Research Promotion Center, Shiga University. [website]
  
  

Research Areas

I am mainly engaged in the following research areas in fundamental and applied studies as well as contribution to services and products, and research and development management: 

• Machine Learning / Data Mining

• AI modeling and Data Analysis related to Intelligent Transportation System, Connected Vehicles, Automonomous Vehicles, and MaaS  (Mobility as a Service)

• Nonlinear Dynamical Systems
  
  

Career History

• 2023/6-: Project Associate Professor, Data Science and AI Innovation Research Promotion Center, Shiga University. [website]

• 2020/11-2022/10: Visiting Researcher, Graduate School of Information Science and Technology, The University of Tokyo. [website]

• 2019/4-2024/3: Visiting Researcher, National Institute of Advanced Industrial Science and Technology (AIST).  [website]

• 2019/4-: Principal Researcher/Project General Manager, Toyota Motor Corporation. [profile] [interview]

• 2015/4-2019/3: Senior Researcher, Toyota InfoTechnology Center, Co.Ltd.

• 2006/4-2015/3: Senior Researcher / Senior Consultant, Fujitsu Research Institute, Co.Ltd.
  
  

Education

• 2017/9-2020/9: Ph.D., Graduate School of Information Science and Technology, The University of Tokyo.

• 2004/4-2006/3: Master, The Department of Complexity Science and Technology, Graduate School of Frontier Sciences, The University of Tokyo.

• 2002/4-2004/3: Bachelor, Department of Physics, Faculty of Science, The University of Tokyo.

• 2000/4-2002/3: Natural Sciences 1, College of Arts and Sciences, The University of Tokyo.

Publications

Preprints

1. Jiawei Wang, Chuang Yang, Jiawei Yong, Xiaohang Xu, Hongjun Wang, Shintaro Fukushima, Noboru Koshizuka, Ryosuke Shibasaki, and Renhe Jiang: Generating urban mobility trajectory with reinforcement learning-enhanced generative pre-trained transformer. [SSRN]

2. Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi, Jiawei Yong, and Shintaro Fukushima: Multimodal point-of-interest recommendation. [arXiv]

3. Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Toyotaro Suzumura, and Shintaro Fukushima: MegaCRN: Meta-graph convolutional recurrent network for spatio-temporal modeling.  [arXiv] [code]

4. Shintaro Fukushima, Atsushi Nitanda, and Kenji Yamanishi: Online robust and adaptive learning from data streams.  [arXiv] [code]
   
   

Papers (Referred)

1. Yusuke Yamaura, Daisuke Kimura, Jiro Nishitoba, Yohei Wakisaka, and Shintaro Fukushima: Continual pre-training of dense retrievers for bridging paraphrases and technical terms in domain-specific retrieval-augmented generation.  In Proceedings of Fortieth AAAI Conference on Artificial Intelligence Workshop (AAAI workshop), 2026. (accepted)

2. Hongjun Wang, Jiawei Yong, Jiawei Wang, Shintaro Fukushima, and Renhe Jiang: Towards resilient transportation: A conditional transformer for accident-Informed traffic forecasting. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026. (accepted)

3. Masaaki Inoue and Shintaro Fukushima: Semantics-aware scene encoder for interpretable active learning in E2E autonomous driving. In Proceedings of Fortieth AAAI Conference on Artificial Intelligence Workshop (AAAI workshop), 2026. (accepted)

4. Aoi Watanabe, Ken Hidaka, Tomoki Nishi, Takeyuki Sasai, and Shintaro Fukushima: Charging behavior differences between BEVs and PHEVs: Evidence from vehicle log data. Transportation Research Part D: Transport and Environment. 150, 105094, 2026. [journal] [SSRN]

5. Jiawei Yong, Takahisa Ishibashi, Yusuke Yamaura, and Shintaro Fukushima: Enhancing text-to-image retrieval with VLM reranking via contrastive reinforcement learning. In Proceedings of International Conference on Computer Vision workshop (ICCV workshop), 2025. (accepted)

6. Haotian Gao, Zheng Dong, Jiawei Yong, Shintaro Fukushima, Kenjiro Taura, and Renhe Jiang: How different from the past?: Spatio-temporal time series forecasting with self-supervised deviation learning. In Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. (accepted)

7. Kan Torii, Shintaro Fukushima, Toshihiro Tanizawa, and Masao Yamanaka: Mobility in high-dimensional space with visualization of massive data. Journal of Information Processing, 2025. (accepted)

8. Kenji Shinoda, Takeyuki Sasai, and Shintaro Fukushima: Popularity‑bias vulnerability: Semi‑supervised label inference attack on federated recommender systems. In Proceedings of the 19th ACM Conference on Recommender Systems (RecSys), pp.660-665, 2025. [proceedings] [code]

9. Hayato Goto, Tomoki Nishi, Takahiro Shiga, Takeyuki Sasai, and Shintaro Fukushima: How to improve PHEV electric mileage ratios? Factor decomposition with explainable AI. Transportation Research Part D: Transport and Environment,  146, 104826, 2025. [journal]

10. Xiaoyan Xu, Yoshikuni Yoshida, Jiawei Yong, Shintaro Fukushima, Renhe Jiang, and Yin Long: Road-level charging gaps and infrastructure enhancements in Japan’s electric vehicle transition. Earth's Future, 13(5), e2024EF005559, 2025. [journal]

11. Shugo Matsusaka, Masaaki Inoue, Hiroya Tsukayama, Hideaki Bunazawa, and Shintaro Fukushima:  A data-driven approach using connected vehicle data to quantify the demand for charging stations. The 31st ITS World Congress, 2025. (accepted)

12. Jiawei Yong, Yusuke Yamaura, Takahisa Ishibashi, and Shintaro Fukushima: Bridging driver descriptions and vehicle warnings: An LLM-based text-to-image retrieval approach. The 31st ITS World Congress, 2025. (accepted)

13. Zhenqin Shi, Akie Sakiyama, Yusuke Yamaura, and Shintaro Fukushima: Fine-grained spatio-temporal traffic congestion detection in motorways with connected vehicle data. The 31st ITS World Congress, 2025. (accepted)

14. Masaaki Inoue, Takeyuki Sasai, and Shintaro Fukushima: Investigating effects of heterogeneity of connected vehicle data. The 31st ITS World Congress, 2025. (accepted)

15. Shintaro Fukushima and Kenji Yamanishi: Graph community augmentation with GMM-based modeling in latent space. In Proceedings of 2024 IEEE International Conference on Data Mining (ICDM), pp.111-120, 2024. [proceedings] [arXiv] (selected as one of the best-ranked papers)

16. Yusuke Yamaura, Yuho Yokoi, Yoshinao Ishii, and Shintaro Fukushima: Empirical data-driven approach to eco-friendly deceleration. In Proceedings of 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.4005-4012, 2024. [proceedings]

17. Hiroki Taniai, Yusuke Yamaura, Shintaro Fukushima, Junichiro Kadomoto, and Hidetsugu Irie: A convenient approach for lane-level congestion detection with on-board camera images and vehicle data. In Proceedings of 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.3417-3424, 2024. [proceedings]

18. Yuta Fukasawa, Kota Yamada, Yoshinao Ishii, Takeyuki Sasai, and Shintaro Fukushima: Estimating reduction in travel time based on large scale driving data from connected vehicles. In Proceedings of 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.972-979, 2024. [proceedings]

19. Yuma Taguchi, Yoshinao Ishii, Takeyuki Sasai, Shintaro Fukushima, and Katsushi Sanda: Network-wide traffic volume estimation using joint matrix factorization with traffic flow conservation law. In Proceedings of 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.952-958, 2024. [proceedings]

20. Katsunori Takahashi, Masatoshi Tokuda, Naoyuki Shinoda, Takayuki Kamei, and Shintaro Fukushima: Efficient and effective data collection and utilization IoT platform for connected cars. The 30th ITS World Congress, 2024. (accepted)

21. Takeharu Hayashi, Yoshinao Ishii, Yasumasa Kobayashi, Tsugumi Otsuka, Shintaro Fukushima: POI search log analysis by text embedding contrastive learning. The 30th ITS World Congress, 2024. (accepted)

22. Yasumasa Kobayashi, Shota Mishima, Kazusa Yoshida, Hideaki Bunazawa, Shintaro Fukushima: Fine-grained spatio-temporal traffic jam detection with connected vehicle driving data. The 30th ITS World Congress, 2024. (accepted)

23. Chihiro Mihara and Shintaro Fukushima: Improvement of corrective operation for in-car voice response systems through sentence clustering. The 30th ITS World Congress, 2024. (accepted)

24. Tsukasa Ishiguro, Takeyuki Sasai, Shintaro Fukushima, and Sei Kato: Leveraging trajectory simplification for efficient map-matching on road network. In Proceedings The 25th IEEE International Conference on Mobile Data Management (MDM), 2024. [proceedings]

25. Varun Varghese, Lichen Luo, Yoshinao Ishii, Shintaro Fukushima, and Makoto Chikaraishi: Does experience affect route choice? An instance-based learning approach using vehicle trajectory data. In Transportation Research Board (TRB), 2024 (accepted).  

26. Shintaro Fukushima and Kenji Yamanishi: Balancing summarization and change detection in graph streams. In Proceedings of 2023 IEEE International Conference on Data Mining (ICDM), pp.1025-1030, 2023. [proceedings] [arXiv]

27. Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong, Masatoshi Hanai, Chuang Yang, Hiroki Kanezashi, Renhe Jiang and Shintaro Fukushima: Revisiting mobility modeling with graph: A graph transformer model for next point-of-interest recommendation. In Proceedings of 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 94, pp.1-10, 2023. [proceedings] [arXiv] [code]

28. Yuta Fukasawa, Yuho Yokoi, Kota Yamada, Takeyuki Sasai, and Shintaro Fukushima: Finding energy-efficient and fast detour routes in unusual traffic events. In Proceedings of 26th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.2618-2625, 2023. [proceedings] 

29. Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, and Toyotaro Suzumura: Spatio-temporal meta-graph learning for traffic forecasting. In Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), pp.8078-8086, 2023. [proceedings] [arXiv] [code] [プレスリリース]

30. Jiawei Yong, Yuichi Wakabayashi, Akihiro Okayasu, Reiji Miki, Takeyuki Sasai, Masaaki Inoue, and Shintaro Fukushima: Estimating total traffic volume with statistical modeling approach. In Proceedings of 25th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp.304-309, 2022. [proceedings]

31. Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, and Chuan hao Lin: Change sign detection with differential MDL change statistics and its application to covid-19 pandemic analysis. Scientific Reports, 11(1):19795, 2021. [journal] [arXiv] [code]

32. Shintaro Fukushima, Ryoga Kanai, and Kenji Yamanishi: Graph summarization with latent variable probabilistic models. In Proceedings of Complex Networks and Their Applications (ComplexNetworks), pp.428-440, 2021. [proceedings]

33. Shintaro Fukushima and Kenji Yamanishi: Detecting hierarchical changes in latent variable models. In Proceedings of 2020 IEEE International Conference on Data Mining (ICDM), pp.1028-1033, 2020. [proceedings] [arXiv] [code]

34. Shintaro Fukushima and Kenji Yamanishi: Detecting metachanges in data streams from the viewpoint of MDL. Entropy, 21(12):1134, 2019. [journal] [code]

35. Kenji Yamanishi and Shintaro Fukushima: Model change detection with the MDL principle. IEEE Transactions on Information Theory, 64(9), pp.6115–6126, 2018. [journal]

36. Ryo Neyama, Shintaro Fukushima, Katsuhiko Miyazaki, and Kazunari Nawa: Detecting lane-change behaviors in urban roads with ego-vehicle sensor data. In Proceedings of The 8th Proceedings of Forum on Web and Database, pp.110–117, 2015 (Japanese Edition).  [proceedings]

37. Shintaro Fukushima and Sunao Murashige: Numerical calculation of topological entropy using turning points of a curve transformed by a map. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition), J90-A(12), pp.932-939, 2007.  [journal]
    
    

Invited Talks

1. Shintaro Fukushima: AI and Data Science in Mobility Domain, IEEE World Technology Summit (WTS), 2025. [website]

2. Shintaro Fukushima:  Activities in AI and data science in the mobility domain, Frontiers of autonomous driving and smart mobility, The Hara Research Foundatation, 2025. [website]

3. Shintaro Fukushima: Data science × Chaos, The 1st Applied Chaos Forum, Applied Chaos Research Group, The Japan Society for Industrial and Applied Mathematics (JSIAM), 2025. [website] 

4. Shintaro Fukushima: Vehicle data analysis and utilization in TOYOTA. Frontiers of autonomous driving and smart mobility 2024, The Hara Research Foundatation, 2024. 

5. Shintaro Fukushima: Issues and Challenges of Anomaly and Its Sign Detection for SM and SHM in Vehicle and Production, Japan Society for Artificial Intelligence, Smart Manufacturing and System Health Management (SIG-SMSHM), 1st Conference on Smart Manufacturing and System Health Management (SIG-SMSHM), 2024. 

6. Shintaro Fukushima: Data Analysis and Utilization of Huge-Scale Vehicle Data, PC Cluster Consortium Workshop, PCCC workshop in Osaka2023 "Big Data and HPC", Osaka, 2023.

7. Shintaro Fukushima: Trends and activities of quality management and assurance of machine learning. Data Science Seminar, Shiga University, Shiga, 2023. 

8. Shintaro Fukushima: Vehicle data analysis and utilization in TOYOTA. Japan Research Institute for New Systems of Society, Tokyo, 2023.

9. Shintaro Fukushima: Advances of machine learning technologies and prospects with the Book "Python Machine Learning." Start Python Club, Tokyo (Online), 2023. 

10. Shintaro Fukushima: Vehicle data analysis in TOYOTA and prospects. Japan Planning Institute, Tokyo, 2022. 

11. Shintaro Fukushima: Trends and activities of quality management and assurance of AI and machine learning. Seminar "Ethical Problems of AI(Artificial Intelligence) and Necessary Actions of Companies", Technical Information Institute Co.,Ltd., Tokyo (Online), 2022. [website]

12. Shintaro Fukushima: A once-in-a-century period of great change: Data analysis and outlook in the connected domain, Graduate School of Information Science and Technology, DSS Symposium, Tokyo (Online), 2022. 

13. Shintaro Fukushima: Trends and activities of quality management and assurance of machine learning. DBSJ Seminar, The Database Society of Japan, Online, 2022. [website] 

14. Shintaro Fukushima: Important points and use cases in data analysis in TOYOTA. DataRobot Japan Seminar, DataRobot Japan Inc., Online, 2021. 

15. Shintaro Fukushima: Trends and activities in materials search and retrosynthetic planning. Materials Informatics Seminar, Institute for Materials Chemistry and Engineering, Kyushu University, and Scientific Research on Innovative Areas: Discrete Geometric Analysis for Material Design B01 Group, Fukuoka (Online), 2021. [website] [leaflet] 

16. Shintaro Fukushima: Trends and activities of quality management and assurance of machine learning. The 2nd AI / IoT System Safety and Security Symposium(AIS^3), National Institute of Informatics (NII) AI/Iot system symposium executive committee et al., Online, 2020. [website] 

17. Shintaro Fukushima: Trends and activities in quality management and assurance of machine learning. PyData.Tokyo, Online, 2020. 

18. Shintaro Fukushima: Prospects in machine learning application: trends and technologies of AI quality assurance, AI Management Conference, Nikon Corporation, Tokyo, 2019. 

19. Shintaro Fukushima: Trends and activities in materials search and retrosynthetic planning. Seminar "Utilization usecases in materials informatics", Technical Information Institute Co., Ltd., Tokyo, 2019. 

20. Shintaro Fukushima: Materials informatics and Python: intesection of condensed matter physics and material science, and data science. PyData.Tokyo One Day Conference, Tokyo, 2018. 

21. Quality assurance of machine learning and AI, panel discussion, Chiba, CEATEC2018. 

22. Shintaro Fukushima: Data analysis in industry and Python/Jupyter Notebook, AI Management Conference, Nikon Corporation, 2018. 

23. Shintaro Fukushima: Python Machine Learning, PyData. Tokyo, Tokyo, 2016. 

24. Shintaro Fukushima: Big data analysis with machine learning, Lunch Seminar, Senshu University, 2016.

25. Shintaro Fukushima: Ideal image and development of human resources for data utilization, 5th Biannual Conference of Transdisciplinary Federation of Science and Technology (Oukan Conference), Tokyo, 2014. [website] 

etc.

Lectures

1. Asako Nagatomi and Shintaro Fukushima: Applied Mathematics III, School of Engineering, Hokkaido University, 2025. 

2. Asako Nagatomi and Shintaro Fukushima: Statistics, School of Engineering, Hokkaido University, 2025. 

3. Shintaro Fukushima: AI and data science. Information science seminar, School of Science and Technology, Kyoto Institute of Technology, 2025. [website]

4. Shintaro Fukushima: Trends and activities in quality management and assurance of machine learning. AI and Information Ethics, Shiga University, 2025. [website]

5. Shintaro Fukushima: AI and data science. Information science seminar, School of Science and Technology, Kyoto Institute of Technology, 2024. [website]

6. Shintaro Fukushima: Trends and activities in quality management and assurance of machine learning. AI and Information Ethics, Shiga University, 2024. [website]

7. Shintaro Fukushima: AI and data science. Information science seminar, School of Science and Technology, Kyoto Institute of Technology, 2023. [website]

8. Shintaro Fukushima: Vehicle data analysis and utilization in Toyota Motor Corporation. Graduate school lecture: Applied Artificial Intelligence and Data Science D, School of Computing Department of Computer Science,, Tokyo Institute of Technology, 2022. [website]

9. Shintaro Fukushima: Key points and case studies in data analysis in TOYOTA. Nagoya University, Mathematical and Data Science Center, Practical Data Scientist Training Program, 2021. [website]

10. Shintaro Fukushima: An introduction to machine learning. Financial dealings and the market economy II, Graduate School of Commerce, Chuo University, 2018.

Patents

1. Zhenqin Shi, Yusuke Yamaura, and Shintaro Fukushima: Information processing device. US20250140105A1, CN119920115A. 

2. Tatsuya Sonobe, Shintaro Fukushima, Yuya Kanehana, and Jiawei YONG: Recommendation method.  US20250027783A1. 

3. Yusuke Yamaura, Tatsuya Sonobe, Yasumasa Kobayashi, and Shintaro Fukushima: Traffic congestion detection system.  US20240395134A1, CN119007429A. 

4. Yusuke Yamaura, Shintaro Fukushima, Takeyuki Sasai, Yuta Fukasawa, Koichi Seki, Jiawei Yong, and Yuho Yokoi: Information processing device.  US20240409095A1. 

5. Jiawei Yong and Shintaro Fukushima: Information processing device, information processing method, and storage medium. US12253377B2. 

6. Shintaro Fukushima: Pattern updating device, pattern update method, and pattern update program. US12292871B2, CN115495117A. 

7. Shintaro Fukushima and Takeyuki Sasai: Machine learning model for image recognition used in autonomous vehicles. US11810338B2, DE102021109276A1, CN113903191B. 

8. Shintaro Fukushima, Zenya Nagata, Akie Sakiyama, Sayaka Yoshizu, and Takeyuki Sasai: Risk prediction device and risk prediction system. US20220009505A1, CN113903191A. 

9. Shintaro Fukushima and Takeyuki Sasai: Search device, learning device, search system, and recording medium. US11403343B2, CN112307241A. 

10. Shingo Fujimoto, Takuro Oshida, Masao Yamanaka, and Shintaro Fukushima: Image processing device and program. US20190095706A1, DE102018123112A1.

Social Contribution Activities

• FY. 2023: Member of Best Author Award Committee of Editorial Board of Bulletin in The Japan Society for Industrial and Applied Mathematics. 

• FY. 2021-2023: Editor of Editorial Board of Bulletin in The Japan Society for Industrial and Applied Mathematics. [website]

• FY. 2018-: Committee Member of AI Quality Management Study Committee, Research and Development on Quality Assurance of Machine Learning AI (under the advanced research themes aimed at social implementation of next-generation artificial intelligence technology by the New Energy and Industrial Technology Development Organization and the National Institute of Advanced Industrial Science and Technology). [website]

  • Machine Learning Quality Management Guideline [1st ed. (2020)] [2nd ed. (2021)] [3rd ed. (2022)] [4th ed. (2023)]

• FY. 2018-2023: Research Collaborator of Discrete Geometric Analysis for Materials Design, Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) (“B01-1 Searching for materials based on analysis of complex networks,” B01 Information Science for Materials Science). [website]