Daiki Nishiyama

I am a Specially Appointed Assistant Professor at the School of Computing, Institute of Science Tokyo.

Institute of Science Tokyo (Apr 2023 - Mar 2026, enrolled as Tokyo Institute of Technology): Doctor of Engineering (Ph.D.). Graduate Major in Artificial Intelligence, Department of Computer Science, School of Computing. Academy of Super Smart Society.
University of Tsukuba (Apr 2021 - Mar 2023): Master of Engineering. Degree Programs in Systems and Information Engineering, Graduate School of Science and Technology.
University of Tsukuba (Apr 2017 - Mar 2021): Bachelor of Engineering. College of Information Science, Major in Intelligent Information Media.

Institute of Science Tokyo (Apr 2023 - Present): Specially Appointed Assistant Professor (JST K-program), School of Computing
Ministry of Defense, Japan Self-Defense Forces (Jul 2023 - Present): Reserved 2nd Lieutenant (Cyber Defense)
TelHi Inc. (Jan 2019 - Present): Head of Creative Business Division
Japan Society for the Promotion of Science (Apr 2024 - Mar 2026): Research Fellow DC2
RIKEN, Center for Advanced Intelligence Project (AIP) (May 2021 - Mar 2026): Part-Time Researcher
NEC Corporation (Aug 2023 - Sep 2023): Research Intern, Secure System Platform Research Laboratories
University of Tsukuba (Apr 2023 - Aug 2023): Technical Assistant
CYBERDYNE Inc. (Oct 2020 - Mar 2023): Web Developer
AVILEN Inc. (Jul 2020 - Mar 2024): Internship
Feedal Inc. (Apr 2019 - Apr 2021): Director, Chief Experience Officer (CxO)

Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma. “CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy.” IEICE Transactions on Information and Systems, Vol.E106-D, No.4, pp.523-537, April 2023. [link]

Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi, and Jun Sakuma. Explainable classifier for malignant lymphoma subtyping via cell graph and image fusion. Proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2025, LNCS 15971, pages 320–330. Springer Nature Switzerland, September 2025. [link]
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma, “CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy.” 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, No. 503, July 2022. [link]

Naoki Kitano, Daiki Nishiyama. “Sentence Pattern Definition and Pattern-Based Method for Extracting Official Names and Abbreviations in Definition and Abbreviation Clauses for Improving Readability of Legal Texts,” Annual Meeting of the Association for Natural Language Processing (NLP2025), B6-5, pp.2243-2248, Nagasaki, March 2025. [link]
Rei Nagaike, Daiki Nishiyama, Youhei Akimoto, Kazuto Fukuchi, Jun Sakuma. “Adversarial Attacks on Hidden Tasks in Multi-Task Learning,” 2024 Symposium on Cryptography and Information Security (SCIS2024), IEICE, January 2024.
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma. “Training Classifiers to Improve Recall of a Specific Class without Accuracy Degradation,” 37th Annual Conference of the Japanese Society for Artificial Intelligence, No. 3D1-GS-2-03, Kumamoto, June 2023. [link]
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma. “Training Classifiers to Improve Recall of a Specific Class Using Additive Angular Margin Loss,” 44th Research Meeting on Information-Theoretic Learning Theory and Machine Learning, IEICE, No. 5, Online, January 2022. [link]
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma. “Class-Sensitive Loss: Improving the Recall of a Particular Class without Sacrificing Accuracy.” 24th Workshop on Information-Theoretic Learning Theory, IEICE, No. 100, Online, November 2021.

Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi, Jun Sakuma. “Statistical Feature Analysis Based on Cells and Explainable Classification Model in Pathological Image Classification of Malignant Lymphoma,” 6th Annual Meeting of the Japanese Medical AI Society, June 2024.

Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi and Jun Sakuma. “Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion.” arXiv preprint arXiv:2503.00925 (2025). [link]
Yu Zhe, Rei Nagaike, Daiki Nishiyama, Kazuto Fukuchi, Jun Sakuma, “Adversarial Attacks on Hidden Tasks in Multi-Task Learning.” arXiv preprint arXiv:2405.15244, 2024. [link]
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma, “CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy.” arXiv preprint arXiv:2209.10920, 2022. [link]

Daiki Nishiyama, “Reliable Classification Model for Explaining Relationships between Objects in Images”. Japan Society for the Promotion of Science, Research Fellowship for Young Scientists 24KJ1049, 2024-2026. [link]

Tokyo Tech Tsubame Scholarship for Doctoral Students (2023)
Program Leader Award, Degree Programs in Systems and Information Engineering, University of Tsukuba (2023)
Special Program Leader Award, Degree Programs in Systems and Information Engineering, University of Tsukuba (2022)
enPiT Excellence Award, Joint Presentation by University of Tsukuba and University of the Ryukyus (2021)
Program Leader Award, College of Information Science, University of Tsukuba (2021)
Winner, 1st TDK Presents Student Innovation Battle (2020)
Best Award, T-ACT 2019 First Half (2019)

Email: nishiyama.d.ac[at]m.titech.ac.jp
Address: E-904, West Building 8, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan