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Yamada Lab
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An algorithm lab that uses mathematics to solve difficult problems in signal processing and optimization.
AboutAbout the lab
Engineering is a paradise for mathematics.
The Yamada Lab studies, from a mathematical standpoint, algorithms for solving problems in signal processing, machine learning, optimization, and inverse problems.
Building on fixed point theory and computer algebra, the lab aims to create and systematize universal algorithms that are broadly applicable to data science and signal processing.
The algorithms the lab develops are applied to inverse problems in fields such as imaging, acoustics, communications, and geophysical exploration.
Note: The lab is not currently recruiting new members.
Key terms
- Inverse problem
- The problem of estimating the cause or original signal from an observed result, such as recovering the original image from a blurred one.
- Convex optimization
- Optimization that finds the minimum of a bowl-shaped function. It makes it easier to reliably find the best solution.
- Sparsity
- The property that most components of the data are zero and only a few components are nonzero.
NewsLab news
Assistant Professor Kume won the Young Researcher Outstanding Presentation Award from the Japan Society for Industrial and Applied Mathematics
A doctoral student won the Excellent Presentation Award at the Young Researchers' Gathering 2026
A master's student won the Student Outstanding Presentation Award from the Operations Research Society of Japan
A doctoral dissertation by a graduate of the lab won the Doctoral Dissertation Award of the Teshima Memorial Research Award
A master's student won both the Outstanding Student Award in Information and Communications Engineering and the Rakusuikai Award
ResearchResearch themes
The lab regards signal processing, machine learning, optimization, and inverse problems as originally one connected field, and works on creating and systematizing universal algorithms that solve problems in them. It develops and applies ways of representing information based on fixed point theory and computer algebra, along with optimization algorithms.
*Illustrative imageSignal Processing with New Mathematical Representations
By boldly combining mathematics that can precisely represent information that cannot be expressed in subspaces with powerful optimization mathematics, the lab aims to realize signal processing algorithms that are dramatically better.
*Illustrative imageConvex Optimization over Fixed Point Sets
The “hybrid steepest descent method,” born in the lab, was the first algorithm in the world to solve convex optimization problems over the fixed point set of a nonexpansive mapping. It also leads directly to solution methods for hierarchical optimization problems.
*Illustrative imageEstimation Models That Exploit Sparsity
For estimation models that use a nonconvex regularizer designed to exploit sparsity (the LiGME regularizer), the lab has clarified the conditions under which the whole model is convex, and has realized algorithms that find globally optimal solutions.
MembersFaculty
The Yamada Lab's members consist of faculty, secretaries, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorIsao YamadaPublications ↗
- Assistant ProfessorKeita KumePublications ↗
VoicesStudent voices
Compiled from answers by students who were in the lab. Ratings and reviews reflect individual students' experiences, not the lab's official views.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
On a 4-point scale, ● marks where students' answers fall
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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: “SAR Kilauea topo interferogram” JPL / Public domain (cropped)
- Signal Processing with New Mathematical Representations: “Fourier series and transform” Lucas Vieira / CC0 (cropped)
- Convex Optimization over Fixed Point Sets: “Gradient descent” Olegalexandrov at English Wikipedia / Public domain (cropped)
- Estimation Models That Exploit Sparsity: “Datacenter Server Racks ( )” Carl Lender from Sunrise, USA / CC BY 2.0 (cropped)
- Related lab (黒田研究室): “Cosine Series Plus Noise TFM” Fourier1789 / CC BY-SA 4.0 (cropped)
- Related lab (山岡・北口研究室): “Network cables and switch” ProjectManhattan / CC BY-SA 3.0 (cropped)
- Related lab (府川研究室): “Cell-Tower” J.smith
（英語版ウィキペディアの 投稿者） / CC BY-SA 2.5 (cropped) - Related lab (スラヴァキス研究室): “The manifold paths of biology ( )” UCL Mathematical & Physical Sciences / CC BY 2.0 (cropped)
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