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Kuroda Lab
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The lab extracts information from data using the mathematics of signal processing and optimization.
AboutAbout the lab
Solving hard signal processing problems with powerful mathematics.
Kuroda Lab studies mathematical models and algorithms for solving fundamental problems in signal processing and optimization.
Signal processing is the field concerned with extracting high-value information from observed data; removing noise from audio and images is one example.
The lab also focuses on applications to real-world problems, centered on wireless communications and remote sensing.
A new lab launched in April 2026, it welcomes visits from students who are considering joining.
Key terms
- Signal processing
- Technology and field of study for extracting high-value information from observed data, for example by removing noise
- Sparsity
- The property that most components of the data are zero and only a few are nonzero
- Convex optimization
- Optimization that finds the minimum of a bowl-shaped function, which makes it easy to reliably find the best solution
NewsLab news
View More ↗ResearchResearch themes
The lab pushes the mathematical frontier, including convex analysis and fixed point theory, to build mathematical models and algorithms that solve fundamental problems in the area where signal processing, optimization, inverse problems, and machine learning overlap. In wireless communications, weather radar, and seismic wave analysis, it conducts joint research with experts in each field.
*Illustrative imageSparse models that also estimate structure
The lab studies models and algorithms that can exploit the "structural sparsity" of unknown information even when that structure is not known in advance. In collaboration with experts in meteorological observation, it works on applications to weather radar.
*Illustrative imageEigenvalue theory of nonlinear mappings and optimization
Combining eigenvalue theory of nonlinear mappings with convex analysis, the lab studies, jointly with the Fraunhofer Heinrich Hertz Institute (Germany), algorithms that find the power allocation that maximizes the communication capacity of wireless networks.
*Illustrative imageEstimation that exploits hidden sparsity
To estimate piecewise continuous signals, including the points where they break, the lab has clarified the principle for extracting the sparsity hidden in them. By recasting the problem as a convex optimization problem, it has realized a method that solves it with little computation.
MembersFaculty
Kuroda Lab members consist of faculty and secretarial staff.As of 2026
- Associate ProfessorHiroki KurodaPublications ↗
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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: “Cosine Series Plus Noise TFM” Fourier1789 / CC BY-SA 4.0 (cropped)
- Sparse models that also estimate structure: “Advanced Technology Demonstrator radar antenna array 01” NOAA / Public domain (cropped)
- Eigenvalue theory of nonlinear mappings and optimization: “Cellular Tower - Knoxville, Tennessee - May 26, 2014” David Ratledge / CC BY 4.0 (cropped)
- Estimation that exploits hidden sparsity: “Triangular wave and square wave on oscilloscope screen (cropped)” User:Xato / CC0 (cropped)
- Related lab (山田研究室): “SAR Kilauea topo interferogram” JPL / Public domain (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)
- 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 (一色研究室): “Circuit image on Rubylith” Camera Operator: TSGT Bob Wickley / Public domain (cropped)
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