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Kano Lab
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The lab aims to build AI systems that can truly be used in the real world.
AboutAbout the lab
AI systems that are truly usable in the real world.
The Kano Lab studies, from fundamentals to applications, technologies for making effective use of AI and machine learning in the real world.
In the real world, various challenges arise, such as limited data and computing resources, changes in the environment, and operational constraints.
The lab identifies these challenges in real-world use and studies new machine learning methods and AI systems that lead to solutions.
As a new lab being established in 2027, it is recruiting members regardless of whether they are undergraduate students, graduate students, or working-professional students.
Key terms
- Machine learning
- Computer technology that finds rules in data and uses them for prediction and decision-making
- Distribution shift
- A difference in the characteristics of the data used to train a model and the data it encounters in actual use
- Robustness
- The property of maintaining stable performance even when there is noise or missing values in the data, or when attacks occur
ResearchResearch themes
The lab aims to realize AI systems that can be trusted and operated under diverse real-world conditions. It identifies challenges such as limited data and computing resources, changes in the environment, and operational constraints, and studies new machine learning methods and AI systems.
*Illustrative imageMachine Learning Robust to Real-World Constraints
The lab studies robust, practical machine learning methods that deliver stable performance even under non-ideal conditions such as limited computing resources, noisy or missing data, distribution shift between training and deployment, and attacks.
*Illustrative imageUnderstanding Why Machine Learning Works
Using both theory and experiments, the lab clarifies why methods that are empirically effective work well and under what conditions their performance degrades. The aim is to determine how far AI can be trusted and used.
*Illustrative imageDesigning Systems for Continued Use
Treating AI as one component of a system, the lab studies the design of mechanisms that can continue to be trusted and used, including evaluating performance during operation, human response when problems occur, and updating models to fit new data.
*Illustrative imageAnalyzing Model Behavior and Discovering Knowledge
The lab analyzes what features and rules a model captures and how it behaves internally, in order to clarify the basis for its decisions and the causes of its failures. It also aims to discover new structures and insights hidden in data.
MembersFaculty
Because the Kano Lab is a new lab being established in 2027, it currently has only faculty members, and no students have been assigned to it yet.As of 2026
- Associate ProfessorRyuichi Kano
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: “Simplified neural network example” Mikael Häggström, M.D. Author info - Reusing images- Conflicts of interest: None Mikael Häggström, M.D. / CC0 (cropped)
- Machine Learning Robust to Real-World Constraints: “Robotics and Machine learning trainings at Fundi bots in Kyanja 11” Kyakwera / CC BY-SA 4.0 (cropped)
- Understanding Why Machine Learning Works: “Example of a deep neural network” BrunelloN / CC BY-SA 4.0 (cropped)
- Designing Systems for Continued Use: “Unattended Monitoring Server (03411515)” IAEA Imagebank / CC BY 2.0 (cropped)
- Analyzing Model Behavior and Discovering Knowledge: “Knowledge graph installation in Futurium” Daniel Mietchen / CC0 (cropped)
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