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Sugiyama-Yokoya-Ishida Lab
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The lab builds the theory and algorithms of machine learning and applies them to real-world problems.
AboutAbout the lab
From machine learning theory to real-world applications
The Sugiyama-Yokoya-Ishida Lab studies the construction of the fundamental theory of machine learning, the development of practical algorithms, and their application to real-world problems.
With advances in information and communication technology, computers have become able to carry out intellectual activities once thought possible only for humans, such as reasoning and creation.
The Sugiyama Lab works on learning theory and algorithms, the Yokoya Lab on visual information processing and remote sensing, and the Ishida Lab on the evaluation and oversight of AI.
The Sugiyama Lab also collaborates with companies and research institutes in Japan and abroad, taking on the challenge of solving real-world problems with cutting-edge machine learning algorithms.
Key terms
- Machine learning
- Technology that lets computers learn rules from data so that they can handle unseen data as well
- Generalization
- The ability to handle unseen situations that a system has not been trained on. It is essential for computers to behave intelligently
- Remote sensing
- Technology for observing conditions at distant locations over a wide area, using satellites, drones, and other platforms
ResearchResearch themes
The lab builds the theoretical foundations of machine learning and develops highly practical algorithms backed by theory. It also conducts research on capturing the real world from image and remote sensing data, and on evaluation methods that measure AI performance.
*Illustrative imageBuilding Learning Theory
Generalization is the ability to handle unseen situations that a system has not been trained on. Based on probability theory, statistics, and related fields, the lab mathematically investigates the mechanisms by which computers acquire generalization ability.
*Illustrative imageDeveloping Learning Algorithms
Machine learning involves a variety of problems, including supervised learning, unsupervised learning, and reinforcement learning, which optimizes long-term decision-making through interaction with an environment. The lab develops highly practical algorithms backed by theoretical grounding.
*Illustrative imageComputational Imaging and Inverse Problems
The lab aims to integrate sensing and computation to overcome hardware limitations such as resolution, noise, and occlusion. It works on recovering the original scene or signal from incomplete, noisy observational data.
*Illustrative imageAI Evaluation
The lab develops data-centric evaluation methods that detect when a benchmark has been "solved" and provide guidance for redesigning it. It aims to build an evaluation foundation that can keep accurately showing the progress of AI even when models reach superhuman performance.
MembersFaculty
The members of the Sugiyama-Yokoya-Ishida Lab consist of faculty, doctoral and master's students, research students, exchange students, project researchers, and project specialist staff.As of 2026
- ProfessorMasashi Sugiyama
- ProfessorNaoto YokoyaPublications ↗
- Associate ProfessorTakashi Ishida
- Associate ProfessorYoichi Ochiai
- LecturerFutoshi Futami
- Assistant ProfessorYifan Zhang
- Project Assistant ProfessorJunjue WangPublications ↗
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: “T-SNE Embedding of MNIST” Kyle McDonald / CC BY 2.0 (cropped)
- Building Learning Theory: “Overfitting regression” GongonR / CC BY-SA 4.0 (cropped)
- Developing Learning Algorithms: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Computational Imaging and Inverse Problems: “HyperspectralCube” Dr. Nicholas M. Short, Sr. / Public domain (cropped)
- AI Evaluation: “MnistExamples” Josef Steppan / CC BY-SA 4.0 (cropped)
- Related lab (宮尾研究室): “Rosetta Stone” Hans Hillewaert / CC BY-SA 4.0 (cropped)
- Related lab (河原林研究室): “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (谷中研究室): “Bookshelves and interior of the library of the Academy for Judiciary” Szeronine / CC BY 4.0 (cropped)
- Related lab (馬研究室): “Unmarked Nissan Rogue - Possible Test Autonomous Vehicle - San Francisco, May 2025 04” 9yz / CC BY 4.0 (cropped)
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