Takeshi Koshizuka
Takeshi Koshizuka
PhD student in Dept. of Computer Science, The University of Tokyo
Member of Issei Sato laboratory
Research fellow at JSPS (DC1)
Google Scholar / Github / Twitter / CV
Takeshi Koshizuka and Issei Sato, "Understanding Generalization in Physics Informed Models through Affine Variety Dimensions", Advances in Neural Information Processing Systems 38 (NeurIPS), 2025. 📄
Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka and Issei Sato, "Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective", Advances in Neural Information Processing Systems 37 (NeurIPS), 2024. 📄
Takeshi Koshizuka and Issei Sato, "Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics", International Conference on Learning Representations (ICLR), 2023, notable-top-25% (Spotlight). 📄 🖥️ ⚒️
Takeshi Koshizuka, Hidefumi Ohmura and Kouichi Katsurada, "Fine-tuning pre-trained voice conversion model for adding new target speakers with limited data.", In Proceedings of Interspeech 2021,1339-1343, doi: 10.21437/Interspeech.2021-244. 📄 🖥️
Yurie Hara, Naho Orita, Deng Ying, Takeshi Koshizuka and Sakai Hiromu, "A neurolinguistic investigation into semantic differences of evidentiality and modality", In Proceedings of Sinn und Bedeutung, Vol. 24, No. 1, pp. 273--290, September 2020. 📄
Takeshi Koshizuka, Koh Takeuchi, Tatsushi Matsubayashi and Hiroshi Sawada, "Graph-based Regional NMF for Distributed Computing", IPSJ Journal, Vol. 62, No. 1, pp. 387--396, 2021. 📄
Takeshi Koshizuka, Ryo Eguchi, Matthew Ishige, Ryo Yonetani, "CARATTE: Camera and Radio-Based Attention Estimation.", Demo presentation at Meeting on Image Recognition and Understanding (MIRU), 2025.
Takeshi Koshizuka, "From Optimal Transport to Schrödinger Bridges: A Variational Perspective on Population Dynamics", REMODEL-DSC WORKSHOP ON MACHINE LEARNING AND NUMERICAL ANALYSIS, Apr 2025. (invited talk)
Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka and Issei Sato, "Why deep fourier neural operator performs worse: Revisiting the edge of chaos.", In Proceedings of Information-Based Induction Sciences and Machine Learning Workshop (IBIS2023), Nov 2023.
Received best student presentation award.
Takeshi Koshizuka, "Diffusion generative model learning based on Schrödinger Bridge Problem" , A New Era in Mathematical Science: The Synergy of Numerical Analysis and Machine Learning, Nov 2023. (invited talk)
Takeshi Koshizuka and Issei Sato, "Neural Lagrangian Schrödinger Bridge for Population Dynamics", In Proceedings of Information-Based Induction Sciences and Machine Learning Workshop (IBIS2022), Nov 2022.
Received outstanding student presentation award.
Takeshi Koshizuka, Hidefumi Ohmura and Kouichi Katsurada, "A Vocoder-free Any-to-Many Voice Conversion using Pre-trained vq-wav2vec", IEICE Technical Report, SP2021-03, Vol. 120, No. 399, pp. 176--181, March 2021. [PDF]
Yurie Hara, Naho Orita, Deng Ying, Takeshi Koshizuka and Sakai Hiromu, "Evidentiality, Modality and Causality--corpus and neurolinguistic studies.", IEICE Technical Report; IEICE Tech. Rep. 119.151 (2019): 15-15.
Takeshi Koshizuka, Koh Takeuchi, Tatsushi Matsubayashi and Hiroshi Sawada, "Graph-based Regional NMF for Distributed Computing", DICOMO2019, pp. 743-751, 2019.
Received best research paper award.
D.Sc. in Comupter Science, The University of Tokyo, Apr. 2023 - Present
M.Sc. in Computer Science, The University of Tokyo, Apr. 2021 - Mar. 2023
B.Sc. in Information Science, Tokyo University of Science, Apr. 2017 - Mar. 2021
Part-time Researcher, AI Lab @ CyberAgent, Inc. Japan, May. 2023 - May 2025
Data Scientist Internship, CyberAgent, Inc. Japan, Mar. 2023
Part-time Machine Learning Engineer, Future Architect, Inc. Japan, Oct. 2020 - Sep. 2021
Research Internship, NTT Communication Science Laboratories, Aug. 2018 - Sep 2018
Research Internship, NTT Communication Science Laboratories, Feb.2018 - Mar. 2018