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Fundamental Data Informatics Lab
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A lab that reads data from complex phenomena and turns it into valuable information.
AboutAbout the lab
A data science approach to complex systems information
The Fundamental Data Informatics Lab researches frameworks, and their applications, for interpreting data collected from complex and diverse natural phenomena, social phenomena, and human activities as valuable information, giving it meaning, and putting it to use.
In particular, the lab mainly deals with cases that are transient and difficult to handle with standard procedures.
The faculty members each work on learning analytics in IT education and e-learning, visualization of learning histories, and the development of statistical data analysis methods.
Key terms
- Learning analytics
- An approach that analyzes and models behavior data recorded during learning, and uses the results to improve education and learning content
- Self-regulated learning
- Learners adjusting how they learn on their own and moving their learning forward autonomously. It is also known as SRL, after its English initials
- Sparse estimation
- A statistical estimation method that selects only the necessary elements from many and tries to explain the data with few elements
NewsLab news
A doctoral student received the Presentation Award at the student session of the Okayama Statistics Study Group
A doctoral student received the Best Student Award at the AIE & GP-DS International Symposium
ResearchResearch themes
The Fundamental Data Informatics Lab researches frameworks for interpreting data collected from complex and diverse natural phenomena, social phenomena, and human activities as valuable information and putting it to use. Faculty members and students each have their own research themes, ranging from analysis of learning behavior in educational settings and development of statistical data analysis methods to analysis of the speech of manzai (Japanese comedy duo performances).
*Illustrative imageLearning analytics in IT education and e-learning
To promote effective learning in IT education and e-learning, the lab develops applications and tools for real educational settings. It analyzes and models the learning behavior recorded in practice.
*Illustrative imageVisualization of learning histories and behavior analysis
To encourage learners to learn autonomously, the lab visualizes learning histories to support self-regulated learning and develops tools suited to educational settings. It analyzes and models the learning behavior data obtained from practice.
*Illustrative imageDevelopment and application of data analysis methods
The lab develops analysis methods based on sparse estimation, nonparametric regression, varying-coefficient models, and similar techniques, along with their estimation algorithms. Anticipating spatial and spatiotemporal data analysis such as real estate price prediction and ecological surveys of marine organisms, it also analyzes real data in cooperation with companies and research institutes.
*Illustrative imageClassifying skill level from the speech of manzai
The lab analyzes acoustic features of manzai, such as speech timing and prosody, to classify performers' skill levels. By comparing professionals and amateurs, it clarifies structural differences in the "ma" (timing and pauses) of dialogue, aiming to apply the findings to dialogue robots and better conversation between people.
MembersFaculty
The members of the Fundamental Data Informatics Lab consist of faculty members, doctoral and master's students, and collaborators.As of 2026
- Associate ProfessorTakashi Mitsuishi
- Assistant ProfessorSatoru Matsumoto
- Assistant ProfessorMineaki Ohishi
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: “ILNumerics Visualization in Array Visualizer” Hpcguy014 / CC BY-SA 4.0 (cropped)
- Learning analytics in IT education and e-learning: “Tabletunterricht 06” Aigner Ronja, Kohlmeier Michelle / CC0 (cropped)
- Visualization of learning histories and behavior analysis: “Hand, bar and pie chart, dark” Maximilianklein / CC BY-SA 4.0 (cropped)
- Development and application of data analysis methods: “An aerial photo of a residential neighborhood on Padre Island” Matthew T Rader / CC BY-SA 4.0 (cropped)
- Classifying skill level from the speech of manzai: “Gooseneck microphone on podium with stage bokeh lights 01” A S M Jobaer / CC BY-SA 4.0 (cropped)
- Related lab (Large-Scale Data Mathematical Optimization Lab): “Network graph visualization showing bibliography and references” Ecpp / CC BY-SA 4.0 (cropped)
- Related lab (High-Performance Computing Lab): “Pleiades supercomputer racks 4” Oleg Alexandrov / CC BY-SA 4.0 (cropped)
- Related lab (Computing Mechanisms Lab): “Fingerprint scanner identification” Rachmaninoff / CC BY-SA 3.0 (cropped)
- Related lab (Fundamental Information Mathematics II Lab): “Sine Wave Domain Coloring Corrected” 12root2 / CC BY-SA 4.0 (cropped)
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