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Okada Lab
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A lab that uses statistical mechanics and machine learning to extract knowledge from data on the brain and materials.
AboutAbout the lab
Finding the underlying mechanisms in vast amounts of data
The Okada Lab studies the mathematical mechanisms behind intelligent activity, from perception to action.
Using methods from machine learning, statistical mechanics, and optimization, the lab focuses mainly on neuroscience and materials science.
Statistical mechanics methods can be very powerful for analyzing systems made up of many interacting components, such as the magnetism of materials or the activity of neurons in the brain.
The lab works on sparse modeling, which extracts knowledge from high-dimensional observational data, and on data-driven science, which finds models directly from data.
Key terms
- Sparse modeling
- An analytical approach that tries to explain large amounts of data using only a very small number of truly important elements
- Data-driven science
- A way of doing science that, instead of starting from theory, finds models that explain phenomena directly from measurement data
- Bayesian measurement
- A method that applies Bayesian inference to the analysis of measurement data, expressing parameter values and the goodness of models as probabilities
- Synchrotron radiation
- Intense light emitted by electrons traveling at nearly the speed of light along a curved path. It is used to investigate the structure of materials and the states of electrons
NewsLab news
A doctoral student received the Student Excellent Presentation Award from the Physical Society of Japan
A doctoral student received a Poster Award at the ISSP Supercomputer Joint Use / CCMS Joint Workshop
A doctoral student received the BRONZE POSTER AWARD at SAS2024
A doctoral student received the Excellent Poster Award at the Young Researchers' Symposium of the Japanese Society of Microscopy
ResearchResearch themes
Using machine learning, information statistical mechanics, and optimization, the lab handles everything from data collection and analysis to choosing among candidate scenarios, aiming to uncover the mathematical structure behind intelligent activity. It collaborates with many labs, both theoretical and experimental, and is extending data-driven science based on Bayesian measurement to a wide range of fields.
*Illustrative imageApplying Bayesian measurement to SPring-8 data
To expand data-driven science widely, the lab is carrying out the "Bayesianization of All SPring-8 Beamlines" project, which brings Bayesian measurement to the data obtained at SPring-8, one of the world's three major synchrotron radiation facilities.
*Illustrative imageBringing spectral data analysis to other fields
The lab is extending data-driven science to the nuclear fusion and astrobiology groups in the Department of Complexity Science and Engineering. As a first step, it is taking up spectroscopy, which is widely used across scales and subjects.
*Illustrative imageVisual processing in the brain and brain-inspired AI
The lab analyzes neural activity data, mainly from the visual cortex, and builds neural circuit models to uncover the principles of information processing in the brain. It aims to apply these findings to the design of next-generation brain-inspired artificial intelligence.
MembersFaculty
The members of the Okada Lab consist of faculty members and doctoral and master's students.
- ProfessorMasato OkadaPublications ↗
- Assistant ProfessorShun KatakamiPublications ↗
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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: “SPring-8” Koji101 / CC BY 3.0 (cropped)
- Applying Bayesian measurement to SPring-8 data: “SPring- img pano” Artorius / CC BY-SA 3.0 (cropped)
- Bringing spectral data analysis to other fields: “High resolution solar spectrum (noao-sun)” N.A. Sharp/KPNO/NOIRLab/NSO/NSF/AURA / CC BY 4.0 (cropped)
- Visual processing in the brain and brain-inspired AI: “Cortical Minicolumn” Quorumetrix / CC BY 4.0 (cropped)
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