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Uchida Lab
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A lab that conducts research moving back and forth between the theory and applications of machine learning.
AboutAbout the lab
Studying the theory and applications of machine learning
The Uchida Lab studies the theory and applications of machine learning.
Moving back and forth between theory and applications, the lab aims to clarify the principles and properties of machine learning mathematically.
It also conducts empirical research using real-world data.
Key terms
- Machine learning
- Technology that lets computers learn patterns and regularities from large amounts of data
NewsLab news
Paper accepted at FMLDS 2026
Received the Outstanding Paper Award at ICAIHE 2026
Paper accepted at ICAIHE 2026
Paper accepted in IEEE Access
Received the Outstanding Poster Presentation Award at the Machine Learning Young Researchers' Meeting
ResearchResearch themes
The Uchida Lab conducts research on the theory and applications of machine learning. Moving back and forth between theory and applications, it aims to understand the principles and properties of machine learning both mathematically and empirically.
*Illustrative imageLearning Robust to Label Errors
The ground-truth labels in data used for machine learning contain errors and inconsistencies. The lab studies learning methods whose predictions do not easily break down even when labels are noisy, by improving label quality using additional information and confidence scores.
*Illustrative imageSafety of Large Language Models
To verify the safety of generative AI, the lab analyzes the threat of "jailbreaks," in which a person intervenes to get around the safeguards of a language model. It also considers methods of controlling the candidate words a model generates in order to steer its output in a desirable direction.
*Illustrative imageSolving Real-World Problems with AI
The lab applies machine learning to data from real-world settings, such as predicting shape in the hot rolling of steel and building systems that support the treatment of smartphone dependence. Using causal structure and summarization, it develops useful prediction and support.
MembersFaculty
- ProfessorMasato Uchida
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: “Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Learning Robust to Label Errors: “Seasonal Adjustment with X-13ARIMA-SEATS program in NumXL” ZaidMarridi / CC BY-SA 4.0 (cropped)
- Safety of Large Language Models: “ERAI Screen” ERNO / CC BY-SA 4.0 (cropped)
- Solving Real-World Problems with AI: “"Hot" Rolling Some Steel ( )” Erik Charlton from Menlo Park, USA / CC BY 2.0 (cropped)
- Related lab (Simo-Serra Lab): “3D rendering example” Adamgmz / CC BY-SA 4.0 (cropped)
- Related lab (Tanaka-Ishii Lab): “Fractal Broccoli” Jon Sullivan / Public domain (cropped)
- Related lab (Sugawara Lab): “Ant robot” Tiia Monto / CC BY 4.0 (cropped)
- Related lab (Sako Lab): “Safe Deposit Boxes” Fixedsun / CC0 (cropped)
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