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Orihara Lab
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A lab that uses machine learning to analyze data on human activity and understand people.
AboutAbout the lab
Understanding people through data on human activity.
The Orihara Lab, a Kioxia collaborative lab, studies the analysis of the many kinds of data generated by human activity.
Human behavior in cyberspace is increasingly recorded as data, and analyzing it may reveal how people make decisions and what preferences they hold unconsciously.
The lab develops machine learning and natural language processing techniques as analytical tools, aiming to understand people more deeply through their access to cyberspace.
Students propose research topics on their own initiative and decide on them with reference to the faculty's advice.
Key terms
- Machine learning
- A technology in which computers learn rules and features from large amounts of data and use them for prediction and decision-making
- Natural language processing
- A technology for analyzing and processing text written in human language by computer
- Generative adversarial network
- A machine learning mechanism used in models that convert an image attribute (such as hair color) into a different attribute
ResearchResearch themes
The lab analyzes data that records human behavior in cyberspace and explores the possibility of revealing how people make decisions and what they prefer. As tools for this, it conducts research to advance machine learning and natural language processing techniques.
*Illustrative imagePredicting Social Media Flaming
This is one of the lab's past research topics. Using machine learning on past cases of flaming, the aim was to predict future flaming and prevent it before it happens. Based on the hypothesis that claims contrary to public opinion lead to flaming, the lab defined features that quantify public opinion on a topic and built a prediction model.
*Illustrative imageFactors Behind Successful Image Domain Translation
This is one of the lab's past research topics. Models using generative adversarial networks (GANs) have been proposed for translations that change attributes such as hair color while preserving the meaning of an image, but their accuracy varies greatly depending on the dataset and domain. By investigating the factors that determine success, the lab found that translation often succeeds when diversity within a domain is relatively low and dissimilarity between domains is relatively high.
MembersFaculty
- Affiliated ProfessorRyohei Orihara
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 people in Hong Kong using smartphones whilst walking” Ccmsharma2 / CC BY-SA 4.0 (cropped)
- Predicting Social Media Flaming: “FIRE 01” MarcusObal / CC BY-SA 3.0 (cropped)
- Factors Behind Successful Image Domain Translation: “Harpy-Hawk-Owl Photo Editing extreme” CulmSee-Axel-acad.2161 / CC BY-SA 4.0 (cropped)
- Related lab (原田 慧 研究室): “Real-time bus tracking control room in Lebanon” UrusHyby / CC0 (cropped)
- Related lab (加納 龍一
研究室): “Simplified neural network example” Mikael Häggström, M.D. Author info - Reusing images- Conflicts of interest: None Mikael Häggström, M.D. / CC0 (cropped) - Related lab (幸島 匡宏 研究室): “Machine Learning cookies” Syced / CC0 (cropped)
- Related lab (新谷 隆彦 研究室): “Xiaomi Mi band 2 18” Nahid.rajbd / CC BY-SA 4.0 (cropped)
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