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Data-Driven Systems Lab
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A lab that develops AI and machine learning methods and applies them to scientific research and manufacturing.
AboutAbout the lab
Machine learning and data science, in theory and in practice.
The Takeuchi-Taji Lab studies AI and machine learning from both theoretical and practical perspectives.
To advance “data-driven” science and technology, which aims to make new discoveries by analyzing large amounts of data, the lab focuses on developing AI and machine learning methods.
In manufacturing, life science, and materials science, the lab conducts research that incorporates AI and machine learning through joint research with companies and researchers in each field.
The lab also works with a team at the RIKEN Center for Advanced Intelligence Project.
Key terms
- Machine learning
- Technology for building computer programs that analyze large amounts of data. The programs learn rules from the data.
- Data-driven approach
- A way of doing research that gains new knowledge by analyzing large amounts of data, following theory, experiment, and simulation.
- Statistical inference
- A method that uses probability to estimate how reliable a result is, based on the data at hand.
NewsLab news
A paper was accepted to the international conference NeurIPS
A paper by PhD students and others was accepted to the journal TMLR
A paper was accepted to the international conference ICML
A paper was published in the journal Mathematics
ResearchResearch themes
The lab conducts research and education in machine learning and puts data-driven approaches into practice in science and technology. Its work rests on two pillars: developing AI and machine learning methods, and using them in scientific research and technology development.
*Illustrative imageGenerating hypotheses with machine learning
To generate promising hypotheses from data that knowledge and experience alone would not suggest, the lab works on theoretical analysis and method development for pattern mining and generative modeling.
*Illustrative imageReliability of machine learning models
Complex models such as deep neural networks make highly accurate predictions, but their explainability and reliability are insufficient. The lab develops methods for performing statistical inference on such models.
*Illustrative imageDesigning experiments with machine learning
Based on insights gained from data analysis, the lab develops methods for systematically planning experiments in scientific research and technology development.
*Illustrative imageManufacturing informatics
Together with industry partners, the lab brings AI and machine learning into the manufacturing field and works to innovate data-driven manufacturing technology.
MembersFaculty
The Takeuchi-Taji Lab's members consist of faculty, researchers from RIKEN, assistant staff, and PhD, master's, and undergraduate students.As of 2026
- ProfessorIchiro Takeuchi
- Associate ProfessorKoichi Taji
- Project Associate ProfessorHiroyuki Hanada
- Assistant ProfessorShion Takeno
VoicesStudent voices
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Report an errorAbout the images
For images provided to us or used with permission, the source is listed on each line. Wikimedia Commons images are illustrative and were not taken at the lab (they are cropped). Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Macintosh Computer Lab” Mbrickn / CC BY-SA 4.0 (cropped)
- Generating hypotheses with machine learning: “Climate data analysis using tSNE method” Orlovma / CC BY 4.0 (cropped)
- Reliability of machine learning models: “Neural network explain” TseKiChun / CC BY-SA 4.0 (cropped)
- Designing experiments with machine learning: “Andrew+ robot” Pocar19 / CC BY-SA 4.0 (cropped)
- Manufacturing informatics: “Industrial production line operating in a large modern factory” Shixart1985 / CC BY 2.0 (cropped)
- Related lab (Cyber Robotics Lab): “Controlling robotic arm ESA15746126” European Space Agency / CC BY-SA 3.0 igo (cropped)
- Related lab (Mobility Systems Lab): “Waymo self-driving car front view.gk” Grendelkhan / CC BY-SA 4.0 (cropped)
- Related lab (Propulsion and Energy Systems Engineering Lab): “Rotating Detonation Engine Operating in Space” 推進エネルギーシステム工学研究グループ / Used with the lab's permission (cropped)
- Related lab (Thermal Control Engineering Lab (2)): “Iss071e582160 (Sept. 3, 2024) --- NASA astronaut and Expedition 71 Flight Engineer Jeanette Epps replaces and cleans research components inside the Destiny laboratory module's Combustion Integrated Rack” NASA Johnson Space Center / Public domain (cropped)
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