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Matsushima Lab
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The lab develops machine learning methods for understanding the big picture of large-scale data.
AboutAbout the lab
Turning data into understandable knowledge
Matsushima Lab studies the foundations of machine learning for understanding data.
Data of many kinds is generated in society every day, including urban infrastructure, healthcare and health, marketing, and materials.
The lab studies techniques that express the analysis results obtained from such large-scale data as knowledge in order to understand the big picture of complex data, along with the optimization algorithms needed for them.
Students can work on both the fundamental theory of machine learning and its practical applications.
Key terms
- Optimization algorithm
- A computational procedure for efficiently finding, among many candidates, the value that best fits the objective
- Interpretable predictive model
- A prediction mechanism whose results come with an explanation, understandable to people, of why that prediction was made
- Causal discovery
- Estimating from data which factors are the causes of which other factors, that is, the causal connections among them
NewsLab news
A paper by a doctoral student has been accepted to Data Mining and Knowledge Discovery
Information, 3rd Edition, to which Associate Professor Matsushima contributed as one of the co-authors, has been published
ResearchResearch themes
The lab studies the foundations of machine learning for understanding data. It researches and develops machine learning techniques that express analysis results obtained from data as knowledge in order to understand the big picture of complex data, along with the optimization algorithms needed for them.
*Illustrative imageMachine Learning for Understanding Data
The lab focuses on data-driven machine learning that captures the big picture of complex data, and conducts research using traffic data and web marketing data. It places importance on being able to compare and explain learning results, and on being able to predict how things change when an intervention is made.
*Illustrative imageInterpretable Predictive Models
The lab focuses on generalized additive models and models based on combinations of two variables, as predictive models whose predictions can come with explanations that people can understand. It aims to develop learning methods that are more efficient than optimization based on coordinate descent, as well as models that are even easier to interpret.
*Illustrative imageExploring Causality through Information Theory
Based on the idea that model selection grounded in information theory is useful for identifying the causal mechanisms underlying data, the lab develops methods for estimating causal mechanisms from data. It aims to develop causal discovery methods that can be applied to more complex and larger-scale data.
MembersFaculty
The members of Matsushima Lab consist of faculty members and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorShin MatsushimaPublications ↗
VoicesStudent voices
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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: “Young woman is cycling over the bike path along the car traffic on the road Weesperpstraat; free photo Amsterdam, Fons Heijnsbroek, July 2022” Fons Heijnsbroek / CC0 (cropped)
- Machine Learning for Understanding Data: “Bikelane 9Av 30 jeh” Jim.henderson / Public domain (cropped)
- Interpretable Predictive Models: “Gradient ascent (contour)” user:Joris Gillis / Public domain (cropped)
- Exploring Causality through Information Theory: “Common cause scenario as a causal model” Terrarium56 / CC0 (cropped)
- Related lab (Kanai Lab): “CSIRO ScienceImage 11431 Computer model image of a rogue wave smashing into a semisubmersible platform weighing around 32000 tonnes” division, CSIRO / CC BY 3.0 (cropped)
- Related lab (Kaneko Lab): “Go (13×” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Related lab (Hiraki Lab): “Babies with soft books” https://pixabay.com/en/users/ / / CC0 (cropped)
- Related lab (Komiya Lab): “Nuvvuagittuq belt rocks” NASA / Public domain (cropped)
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