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Mathematical Engineering Lab
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The lab clarifies the mathematical form of problems in computing and communications.
AboutAbout the lab
Revealing the mathematical form of problems
The Mathematical Engineering Lab aims to clarify the mathematical form of a wide range of problems in computers and digital communications, and to derive universal solutions based on that understanding.
Its research themes are basic theories such as learning theory, machine learning, information theory, information geometry, communication theory, network theory, and nonlinear systems theory, as well as their applications.
Examples of applications include cyber attack detection on the Internet, super-resolution, pattern recognition, CDMA communications, and ultra-high-resolution analog-to-digital conversion.
On the lab's website, the Takeuchi, Jitsumatsu, and Hanaka labs are introduced together under the name "Mathematical Engineering and Information Circuits Lab."
Key terms
- MDL principle
- A approach to learning in which the rule that describes the data with the shortest description length is chosen (the minimum description length principle)
- Super-resolution
- A technology that improves image quality in digital cameras and video recorders beyond the performance of the device
- Botnet
- A network formed by malicious programs called bots
NewsLab news
A paper by a doctoral student has been accepted to IEEE VTC2026-fall
A doctoral student received the Excellent Presentation Award at the Young Researchers' Meeting 2026
A paper by Professor Yutaka Jitsumatsu and a doctoral student has been accepted for publication in IEEE Wireless Communications Letters
A paper by a doctoral student has been accepted to IEEE VTC2026-Spring
A paper by Professor Yutaka Jitsumatsu and a doctoral student has been accepted to IEEE ICASSP2026
ResearchResearch themes
The lab works on basic theories such as learning theory, information theory, and communication theory, as well as their applications. Its research pages are divided into the Takeuchi Lab (machine learning), the Jitsumatsu Lab (digital communications), and the Hanaka Lab (graph optimization). This page introduces the themes of the first of these, the Takeuchi Lab.
*Illustrative imageThe relationship between data compression and machine learning
The lab studies the relationship between data compression and machine learning from a theoretical perspective. In collaboration with Yale University, it conducts basic research on stochastic complexity, which gives the limit of how far data can be compressed, and evaluates the generalization error of MDL estimators.
*Illustrative imageError-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 becoming increasingly active worldwide. The lab studies sparse superposition codes, which are closely related to machine learning.
*Illustrative imageCyber attack detection
With the goal of predicting increasingly serious cyber attacks, 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 detecting botnets by applying the MDL principle.
*Illustrative imageImage analysis, super-resolution, and MRI
The lab applies machine learning to image analysis. It currently focuses on super-resolution, which improves image quality beyond the performance of the device, and on speeding up MRI (magnetic resonance imaging), using deep learning in both.
MembersFaculty
Members of the Mathematical Engineering Lab consist of faculty, a visiting assistant professor, administrative assistants, doctoral, master's, and undergraduate students, and research students.As of 2026
- Senior 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: “Vodno Telecommunication Tower” kallerna / CC BY-SA 4.0 (cropped)
- The relationship between data compression 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)
- Error-correcting codes: “QR Code Damaged” QR code for English Wikipedia.svg: brdall. Original uploader was Brdall at en.wikipedia. Later version(s) were uploaded by Sertion, Johndrinkwater, Ke4roh at en.wikipedia. derivative work: Bobmath (talk) / CC0 (cropped)
- Cyber attack detection: “US Navy 090310-D-5972N-009 Information Systems Technician 2nd Class Ryan Allshouse uses the intrusion detection system to monitor unclassified network activity from the automated data processing workspace” U.S. Navy photo by Rick Naystatt / Public domain (cropped)
- Image analysis, super-resolution, and MRI: “MRI of scoliosis, case 02. 5” Jmarchn / CC BY-SA 3.0 (cropped)
- Related lab (情報システムセキュリティ研究室): “IDF Enclosure for LAN and Switches” DoulosBen / CC BY 4.0 (cropped)
- Related lab (先端ネットワーク研究室): “Spider web Luc Viatour” Luc Viatour / CC BY-SA 3.0 (cropped)
- Related lab (情報セキュリティー & マルチメディアセキュリティ研究室): “Firewall-X400” Cuda-mwolfe / CC BY-SA 4.0 (cropped)
- Related lab (高性能計算研究室): “IBM Blue Gene P supercomputer” Argonne National Laboratory's Flickr page / CC BY-SA 2.0 (cropped)
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