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Mathematical Engineering and Information Circuits Lab
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The lab explores problems in machine learning and digital communications from a mathematical perspective.
AboutAbout the lab
Revealing the mathematical form of problems in computing and communications
The Mathematical Engineering and Information Circuits Lab aims to reveal the mathematical form of various problems in computers and digital communications, and to derive universal solutions based on it.
Its research themes are fundamental theories and their applications, including learning theory, machine learning, information theory, information geometry, communication theory, network theory, and nonlinear systems theory.
Examples of applications mentioned include cyberattack detection, super-resolution, pattern recognition, CDMA communications, and ultra-high-precision analog-to-digital conversion.
On the lab's website, its research is introduced under the names Takeuchi Lab, Jitsumatsu Lab, and Hanaka Lab.
Key terms
- MDL Principle
- MDL principle: the idea that choosing the rule with the shortest description length leads to appropriate learning. Also called the minimum description length principle
- Stochastic Complexity
- Stochastic complexity: a quantity that gives the limit of how far given data can be compressed
- Botnet
- Botnet: a network formed by malicious programs called bots
- Super-Resolution
- Super-resolution: a technology that improves image quality beyond the capability of the device
NewsLab news
A paper by a doctoral student was accepted at IEEE VTC2026-Fall
A doctoral student won the Outstanding Presentation Award at the Young Researchers' Meeting 2026
A paper by a doctoral student and Prof. Jitsumatsu was accepted for publication in IEEE Wireless Communications Letters
A paper by a doctoral student and Prof. Jitsumatsu was accepted at IEEE VTC2026-Spring
A paper by a doctoral student and Prof. Jitsumatsu was accepted at IEEE ICASSP 2026
ResearchResearch themes
The lab studies problems in information fields, such as machine learning, digital communications, and graph (network) algorithms, from a mathematical perspective. Here we introduce the research themes of Takeuchi Lab, which is listed first on the lab's research page.
*Illustrative imageThe MDL principle and machine learning
The lab considers the relationship between data compression and machine learning theoretically. Together with Yale University (USA), it conducts fundamental research on stochastic complexity, which gives the limit of how far given data can be compressed, and on evaluating the generalization error of MDL estimators.
*Illustrative imageResearch on error-correcting codes
LDPC codes, polar codes, and sparse superposition codes have been proposed as error-correcting codes that achieve channel capacity, and research on them is active worldwide. The lab studies sparse superposition codes, which are closely related to machine learning.
*Illustrative imageCyberattack detection
With the goal of predicting increasingly serious cyberattacks, the lab conducts applied research using machine learning. In joint research with the National Institute of Information and Communications Technology, Yokohama National University, Waseda University, and others, it has proposed methods such as one that applies the MDL principle to discover botnets.
*Illustrative imageApplying machine learning to image analysis
The lab conducts research on applying machine learning to image analysis. It currently focuses on introducing deep learning into super-resolution, which raises image quality beyond the capability of the device in digital cameras and video recorders, and into speeding up MRI (magnetic resonance imaging).
MembersFaculty
The members of the Mathematical Engineering and Information Circuits Lab consist of faculty, a visiting assistant professor, administrative assistants, students in the doctoral, master's, and undergraduate programs, and research students.As of 2026
- ProfessorJunichi Takeuchi
- ProfessorYutaka Jitsumatsu
- Associate ProfessorTesshu Hanaka
- Assistant ProfessorYoshinari Takeishi
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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: “Communication theory” JuanOF9 / CC BY-SA 4.0 (cropped)
- The MDL principle and machine learning: “Unraveling AI Complexity - A Comparative View of AI, Machine Learning, Deep Learning, and Generative AI” PopovaZhuhadar / CC BY-SA 4.0 (cropped)
- Research on error-correcting codes: “Conv code 177 133” Kirlf / CC BY-SA 4.0 (cropped)
- Cyberattack detection: “Firewall (networking)” Luis F. Gonzalez / Public domain (cropped)
- Applying machine learning to image analysis: “Concept of machine learning” Ultrabem / CC BY-SA 4.0 (cropped)
- Related lab (Stringology Lab): “A diagram of the Aho-Corasick string search algorithm extracted” User:Algorithmist / CC0 (cropped)
- Related lab (Algorithm Theory Lab): “Jungheinrich Autonomous Robot arculee M” Marc Auledas Noguera (Auledas) / CC BY 4.0 (cropped)
- Related lab (Quantum Information Lab): “Heat engine summary” Brundl15 / CC BY-SA 4.0 (cropped)
- Related lab (Information Security & Multimedia Security Lab): “Homomorphic Encryption Setup” Aaadir / CC0 (cropped)
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