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Takabe Lab
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A lab that approaches information processing through statistical physics and machine learning.
AboutAbout the lab
Information processing that starts from “many”
The Takabe Lab studies information engineering in a broad sense from the perspectives of machine learning, information theory, and statistical mechanics.
Its subjects include signal processing, optimization, error-correcting codes, network science, and statistical machine learning.
It is particularly interested in the average properties of randomly generated problems and systems.
For such problems, the lab proposes and analyzes efficient, high-performance algorithms.
Key terms
- Statistical mechanics
- A branch of physics that uses probability to study the collective behavior of systems made up of many components
- Deep unfolding
- A technique that unrolls an algorithm with fixed steps into layers, like a stack of neural network layers, and uses deep learning to train the tunable parameters inside it
- Phase transition
- A phenomenon in which a slight change in conditions abruptly changes the properties of the whole system
NewsLab news
A paper by Associate Professor Takabe et al. was published in IEICE Transactions on Fundamentals of Electronics, Communic…
A paper by Assistant Professor Arai et al. was published in Scientific Reports
A paper by Associate Professor Takabe et al. was published in Journal of the Physical Society of Japan
A paper by Associate Professor Takabe et al. was published in IEEE Wireless Communications Letters
ResearchResearch themes
The lab studies information engineering in a broad sense (signal processing, optimization, error-correcting codes, network science, statistical machine learning, and more) from the perspectives of machine learning, information theory, and statistical mechanics. It is particularly interested in the average properties of randomly generated problems and systems, and in proposing and analyzing efficient, high-performance algorithms for them.
*Illustrative imageAlgorithms learned through deep unfolding
Using deep learning, and in particular a technique called deep unfolding, the lab proposes “trainable algorithms” for optimization, wireless communications, and signal processing.
*Illustrative imageTypical-case hardness of combinatorial optimization
The lab analyzes how hard combinatorial optimization problems typically are, and how accurate approximation algorithms typically are.
*Illustrative imageApproximate algorithms for statistical inference
For statistical inference problems, the lab proposes efficient approximate algorithms such as belief propagation and analyzes their performance.
*Illustrative imagePhase transitions in random networks
The lab investigates phase transition phenomena on randomly connected networks using ideas from statistical mechanics.
MembersFaculty
The members of the Takabe Lab consist of faculty members and students in the doctoral, master's, and undergraduate programs.As of 2026
- Associate ProfessorSatoshi TakabePublications ↗
- Assistant ProfessorShunta AraiPublications ↗
- Project Assistant Professor奥山 真佳
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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: “Ising-tartan” Linas / CC BY-SA 3.0 (cropped)
- Algorithms learned through deep unfolding: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Typical-case hardness of combinatorial optimization: “Optimal tour traveling salesman problem Germany” Prof. William Cook / Public domain (cropped)
- Approximate algorithms for statistical inference: “Factor graph” Kibblebrain / CC0 (cropped)
- Phase transitions in random networks: “Erdos generated network-p0.01” w:User:Vonfrisch / CC BY-SA 3.0 (cropped)
- Related lab (金森敬文研究室): “T-SNE Embedding of MNIST” Kyle McDonald / CC BY 2.0 (cropped)
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- Related lab (山下真研究室): “Rosenbrock function 2” RokerHRO / Public domain (cropped)
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