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Kawarabayashi Lab
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The lab pursues the foundations and theory of algorithms and discrete mathematics, with graphs as its main object of study.
AboutAbout the lab
Foundations and theory first, applications later.
The Kawarabayashi Lab conducts research centered on algorithms and discrete mathematics.
Rather than aiming for applications from the start, the lab focuses on fundamental and theoretical research.
In algorithms research, the lab works on everything from improving computational complexity and theoretically analyzing correctness to developing techniques that make implementations fast.
In discrete mathematics research, the lab explores mathematically deep theory while also developing techniques that can be used in other fields, such as algorithms.
Key terms
- Discrete mathematics
- The field of mathematics that deals with discrete values and structures, such as integers and graphs
- Graph
- A representation of connections between objects using points (vertices) and lines (edges) that join them
- NP-hard
- Belonging to a family of especially difficult problems for which no efficient solution method has been found
NewsLab news
Prof. Kawarabayashi elected a 2025 ACM Fellow
Prof. Kawarabayashi receives the Humboldt Research Award
ResearchResearch themes
The lab conducts research centered on algorithms, discrete mathematics, and their applications. Focusing especially on graphs, it develops algorithms from both the theoretical and the implementation side.
*Illustrative imageFast and Accurate Ways to Solve Graph Problems
The lab develops algorithms for graph problems that are theoretically fast and also accurate. It also conducts research proving that problems are NP-hard.
*Illustrative imageAlgorithms for Real-World Graphs
For graphs that arise in the real world, the lab uses theoretical tools such as mathematics to develop algorithms that can be implemented to run fast.
*Illustrative imageSpeeding Up Algorithms on Restricted Classes of Graphs
The lab develops algorithms that run fast, in theory or in practice, when the graphs under consideration are restricted to a particular class, such as planar graphs or social networks.
*Illustrative imageApplying Graph Algorithms to Machine Learning
The lab applies its knowledge of graph algorithms and their implementation to machine learning, especially online learning and deep learning.
MembersFaculty
The Kawarabayashi Lab consists of faculty members, researchers, and doctoral and master's students.As of 2026
- ProfessorKen-ichi Kawarabayashi
- Assistant ProfessorKoyo HayashiPublications ↗
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: “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Fast and Accurate Ways to Solve Graph Problems: “Petersen-graph” PSL27 / CC BY-SA 4.0 (cropped)
- Algorithms for Real-World Graphs: “Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Speeding Up Algorithms on Restricted Classes of Graphs: “Four color theorem map of Germany 3” Tomwsulcer / CC0 (cropped)
- Applying Graph Algorithms to Machine Learning: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Related lab (Sugiyama-Yokoya-Ishida Lab): “T-SNE Embedding of MNIST” Kyle McDonald / CC BY 2.0 (cropped)
- Related lab (Yoshimoto Lab): “150601 es 1s” JAMSTEC / CC BY-SA 4.0 (cropped)
- Related lab (Shinagawa Lab): “Alx source snippet” Tino / CC BY-SA 4.0 (cropped)
- Related lab (Ma Lab): “Unmarked Nissan Rogue - Possible Test Autonomous Vehicle - San Francisco, May 2025 04” 9yz / CC BY 4.0 (cropped)
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