Team Director
Ryosuke Kojima Ph.D.

Laboratory for Multimodal AI Framework

LocationKobe / Integrated Innovation Building

E-mailryosuke.kojima@riken.jp

Overview
Selected Publications
Members

What’s New

Mar. 31, 2025 BDR News

Three new team leaders join RIKEN BDR

Open Positions

Seeking a Postdoctoral Researcher (K24105)

Seeking a Technical Staff II (K24097)

Seeking Research Scientist or Postdoctoral Researcher (K24068)

Towards AI platform technologies for multimodal data in life sciences

The Laboratory for Multimodal AI Framework is developing AI technologies to handle multimodal and hierarchical data, such as images, natural language, acoustic signals, time-series data, and structured data, while applying these technologies to address diverse challenges in life sciences. Specifically, we focus on developing modeling techniques for complex data and large-scale foundational models. Additionally, we aim to translate these advancements into tools and platforms, ultimately deploying them in real-world applications.

Research Theme

Technology development and method/theoretical research for large-scale models for each modality
Technology development and method/theoretical research for large-scale multimodal models
Research and development of tools and platforms for the use of large-scale models in the field

Selected Publications

Kojima R, Okamoto Y.
Learning deep input-output stable dynamics.
Advances in Neural Information Processing Systems 35, 8187-8198 (2022) doi: 10.48550/arXiv.2206.13093

Ishida S, Terayama K, Kojima R, et al.
AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.
Journal of Chemical Information and Modeling 62(6), 1357-1367 (2022) doi: 10.1021/acs.jcim.1c01074

Nakamura K, Kojima R, Uchino E, et al.
Health improvement framework for actionable treatment planning using a surrogate Bayesian model.
Nature Communications 12(1), 3088 (2021) doi: 10.1038/s41467-021-23319-1

Kojima R, Ishida S, Ohta M, et al.
kGCN: a graph-based deep learning framework for chemical structures.
Journal of Cheminformatics 12(1), 32 (2020) doi: 10.1186/s13321-020-00435-6

Kojima R, Sugiyama O, Hoshiba K, et al.
HARK-Bird-Box: A Portable Real-Time Bird Song Scene Analysis System
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (2018) doi: 10.1109/IROS.2018.8594070

Kojima R, Sato T.
Learning to rank in PRISM
International Journal of Approximate Reasoning 93, 561-577 (2018) doi: 10.1016/j.ijar.2017.11.011

Kojima R, Sugiyama O, Suzuki R, et al.
Semi-Automatic Bird Song Analysis by Spatial-Cue-Based Integration of Sound Source Detection, Localization, Separation, and Identification.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (2016) doi: 10.1109/IROS.2016.7759213

Kojima R, Sato T.
Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation
In: Davis J, Ramon J (eds) Inductive Logic Programming, Springer (2015) doi: 10.1007/978-3-319-23708-4_6

Members