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Statistical Informatics Lab
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A lab whose work ranges from statistical theory to data analysis in neuroscience and earthquake research.
AboutAbout the lab
Statistical modeling, in both theory and application.
The Statistical Informatics Lab studies methods of statistical modeling from both theoretical and applied perspectives.
Its theoretical pillars are statistical inference theory, Bayesian theory, statistical model selection, and information geometry.
In collaboration with neuroscience researchers, the lab builds statistical models suited to analyzing neural spike data and EEG.
It also pursues research on data assimilation, which integrates observational data and simulations of earthquakes and tsunamis.
Key terms
- Bayesian theory
- A statistical framework in which prior beliefs are updated with new data as probabilities are reasoned about
- Information geometry
- A field that treats collections of probability distributions as geometric objects and uses the tools of geometry to approach problems in statistics and information theory
- Data assimilation
- A technique that combines numerical simulations with observational data to build models capable of predicting the future
NewsLab news
A master's student received the Student Excellent Presentation Award at the Spring Meeting of the Japan Statistical Society
A team of graduate students received a Special Prize in a data science contest
An alumnus received the Young Researcher Encouragement Award at the Annual Meeting of the Association for Natural Language Processing
An alumnus received the Outstanding Presentation Award at the Joint Statistical Meeting of Japan
Associate Professor Nagao and colleagues received the Outstanding Paper Award from the Japanese Society of Applied Statistics
ResearchResearch themes
The lab studies methods of statistical modeling from both theoretical and applied perspectives, through statistical inference theory, Bayesian theory, statistical model selection, information geometry, and more. It also works on data analysis in collaboration with neuroscience researchers, and on integrating data related to earthquake and tsunami disasters.
*Illustrative imagePrediction Theory
Making predictions from the data at hand is a fundamental problem in statistics. The lab studies methods that predict with a probability distribution rather than a single predicted value, using Bayesian theory, model selection, and information-geometric methods.
*Illustrative imageStatistical Modeling in Neuroscience
In collaboration with neuroscience researchers, the lab constructs statistical models that fit the characteristics of the data. Its work has included analyzing neural spike data and EEG with point process models, and decomposing the periodic components of time series with state-space models.
*Illustrative imageInformation Geometry
This field uses Riemannian geometry to intuitively capture the behavior of estimators and the distances between distributions that appear in statistics and information theory. It is applied to the choice of prior distributions in Bayesian statistics, to machine learning, to coding theory, and more.
*Illustrative imageIntegration of Earthquake and Tsunami Disaster Information
The lab integrates the vast amounts of observational data and numerical simulation data related to earthquakes, tsunamis, and disasters, using statistical methods such as data assimilation. Its aim is to produce knowledge that protects lives and property from major earthquakes.
MembersFaculty
The members of the Statistical Informatics Lab consist of faculty, PhD and master's students, undergraduates, and secretaries.As of 2026
- ProfessorFumiyasu KomakiPublications ↗
- Associate ProfessorTakeru MatsudaPublications ↗
- Associate ProfessorHiromichi NagaoPublications ↗
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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: “Beta distribution for 3 different prior probability functions - J. Rodal” Dr. J. Rodal / CC BY-SA 3.0 (cropped)
- Prediction Theory: “Combined Cumulative Distribution Graphs” ShristiV / CC BY-SA 4.0 (cropped)
- Statistical Modeling in Neuroscience: “MRI brain sagittal section” everyone's idle / CC BY-SA 2.0 (cropped)
- Information Geometry: “Pseudosphere” Win / CC BY-SA 3.0 (cropped)
- Integration of Earthquake and Tsunami Disaster Information: “Tōhoku Seismogram - Flickr - Joe Parks” Joe Parks from Berkeley, CA / CC BY 2.0 (cropped)
- Related lab (数理データ科学研究室): “Rosenbrock function 2” RokerHRO / Public domain (cropped)
- Related lab (学習数理情報学研究室(山西研究室)): “CERN Server 03” Florian Hirzinger - www.fh-ap.com / CC BY-SA 3.0 (cropped)
- Related lab (計算情報学研究室): “SocialNetworkAnalysis” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (数理生命情報学研究室): “Mouse Embryo Cerebellum ( )” NIH Image Gallery from Bethesda, Maryland, USA / Public domain (cropped)
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