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Statistical Mathematics Lab
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The lab studies statistics, capturing the variability of data mathematically to extract useful information.
AboutAbout the lab
Capturing the uncertainty of data with mathematics
The Statistical Mathematics Lab studies statistical science methods that mathematically capture the variability and uncertainty of data and extract useful information and patterns from it.
The lab mainly deals with the theory and application of statistical methods, practical research in biostatistics, and statistical graphics.
The lab develops statistical models for high-dimensional data with complex structures and for longitudinally observed data, and also works on analyzing human data.
On the lab's website, it is introduced under the name "Araki Lab."
Key terms
- high-dimensional data
- Data in which the number of items measured for a single subject is very large. Its structure is complex, and it is hard to handle as it is
- functional data analysis
- A method that smooths longitudinal observations for each subject, represents them as a single function, and extracts information from the collection of such functions
- causal inference
- A statistical approach to examining from data how a factor affects an outcome
NewsLab news
View More ↗ResearchResearch themes
Data is an important source of information, but it is uncertain and variable. This lab captures that uncertainty mathematically, and studies the development and application of methods that efficiently extract useful information and patterns from complex high-dimensional data.
Statistical Modeling
The lab develops statistical models that extract useful information from high-dimensional data with complex structures while minimizing information loss. It also devises criteria for evaluating how good an estimated model is, and confirms the usefulness of its methods through numerical experiments.
*Illustrative imageFunctional Data Analysis
Functional data analysis is an approach that represents data measured over time as a single function and extracts information and patterns from it. Aiming at models that are useful for diverse and complex data, the lab studies prediction, classification, dimension reduction, and causal inference.
*Illustrative imageBiostatistics
This research mathematically captures the uncertainty in human data so that diagnosis and treatment decisions can be made on scientific evidence. The lab works at the researcher level with educational and research institutions and medical institutions outside the university.
*Illustrative imageStatistical Graphics
Statistical graphs are effective for communicating the results of statistical analysis to researchers who are not statisticians. The lab studies graphs that make statistical theory clearly visible when expressing the results of applying statistical models.
MembersFaculty
The members of the Statistical Mathematics Lab are faculty, doctoral and master's students, and special research students.As of 2026
- ProfessorYuko Araki
- Associate ProfessorUtako Yamamoto
- Project Assistant ProfessorGUAN XIN
- Project Assistant ProfessorAo Huang
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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: “Tabuleiros de Galton (antes e depois)” Exhibit made by Estes Objethos Atelier, photo by Rodrigo Tetsuo Argenton / CC BY-SA 4.0 (cropped)
- Functional Data Analysis: “Atmospheric Carbon Dioxide Concentrations (SVS5447)” NASA's Scientific Visualization Studio - NASA/GSFC/Mark SubbaRao, ADNET Systems, Inc./Laurence Schuler, ADNET Systems, Inc./Ian Jones, NASA/GSFC/Peter H. Jacobs / Public domain (cropped)
- Biostatistics: “Stethoscope in clinic” Fatimah Bello / CC0 (cropped)
- Statistical Graphics: “Example of a Stacked Bar Plot in Healthcare Data Analysis using R Software” Monika Wahi / CC BY-SA 4.0 (cropped)
- Related lab (System Information Mathematics II Lab): “Example absolute value” Manuelzapata04 / CC BY-SA 4.0 (cropped)
- Related lab (Intelligent Robotics and AI Lab): “US Navy 100531-N-7676W-075 Visitors interact with the , dexterous, social (MDS) robot Octavia at the Office of Naval Research (ONR) exhibit during Fleet Week New York 2010” U.S. Navy photo by John F. Williams / Public domain (cropped)
- Related lab (Information Biology Lab): “Model of the Anton Dohrn (Museum Windstärke 10 Cuxhaven)” Wikitarisch / CC BY 4.0 (cropped)
- Related lab (Sound Information Science Lab): “Anechoic chamber-1” Togabi / CC BY-SA 4.0 (cropped)
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