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Kawahara Lab
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The lab explores machine learning, from its principles to applications in science and engineering.
AboutAbout the lab
Exploring machine learning, from principles to applications
The Kawahara Lab studies machine learning, the foundational field that underpins AI technologies spreading rapidly through society and industry.
The lab explores new principles, algorithms, and models of machine learning, and applies the results to a wide range of problems in science and engineering.
Recently, it has been working on further advancing deep learning, representation learning, and reinforcement learning, among other topics.
On the lab's website, it is introduced as the Machine Learning Systems Theory Group.
Key terms
- Machine learning
- Technology that has computers find regularities in data and use them for intelligent information processing
- Reinforcement learning
- A machine learning method in which a computer learns how to act well through trial and error
- Surrogate modeling
- Building simple predictive models that can stand in for laborious computations or experiments
NewsLab news
Paper on offline reinforcement learning accepted at NeurIPS'26, an international machine learning conference
Paper on EEG decoding accepted at Communications Medicine
Paper on speeding up smoke animation accepted to the Posters track at SIGGRAPH Asia 2026
Paper accepted at ICONIP'26, an international machine learning conference
Paper on physics-inspired transformer quantum states accepted at Physical Review Research
ResearchResearch themes
The lab explores new principles, algorithms, and models for machine learning, which underpins AI technologies spreading through society and industry. It also applies the results to diverse problems in science and engineering, pursuing research that leads to the discovery of new scientific knowledge and the solution of real-world problems.
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MembersFaculty
The members of the Kawahara Lab consist of faculty, research fellows, administrative assistants, doctoral, master's, and undergraduate students, research students, and members from RIKEN AIP.As of 2026
- ProfessorYoshinobu Kawahara
- Associate ProfessorMasahiro Ikeda
- Project Associate ProfessorHiroshi Morioka
- Project LecturerTakuya Konishi
- Assistant ProfessorMasahiro Fujisawa
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Advancing and Applying Machine Learning: “The relationship and main types of artificial intelligence, machine learning and deep learning” Wang, Tianming, Zhu Chen, Quanliang Shang, Cong Ma, Xiangyu Chen, and Enhua Xiao / CC BY 4.0 (cropped)
- Related lab (Senoo Lab): “Fluidigm C1 Single-Cell Auto Prep system” Konrad Förstner / CC BY 2.0 (cropped)
- Related lab (Tsuchiya Lab): “NOIRLab HQ Server Racks (6V6A0402-CC)” NOIRLab/NSF/AURA/T. Slovinský / CC BY 4.0 (cropped)
- Related lab (Nakashima Lab): “Sample view of data collection process using artificial intelligence application” Captain Andrew M. Freeman, Air Force Institute of Technology / Air Force Research Laboratories (AFRL), Sensors, ATR, Target Recognition Branch. / Public domain (cropped)
- Related lab (Wakamiya Lab): “A starling murmuration at Eyemouth - geograph.org.uk - 6378647” Walter Baxter / CC BY-SA 2.0 (cropped)
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