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Okamoto Lab
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The lab does data science research, including recommender systems, to find useful insights from data.
AboutAbout the lab
Discovering and applying useful insights from data
The Okamoto Lab studies how to discover useful insights from data and put them to use.
Combining statistics, computer science, machine learning, and computational intelligence with real-world domain knowledge, the lab researches and develops techniques and applications for collecting and analyzing behavior logs such as purchase histories and access logs.
The lab is particularly interested in collaborative filtering, an approach to information recommendation, and in building predictive models for value estimation.
Undergraduate students are assigned to the lab after going through the Class I graduation research lab assignment (second semester of third year).
Key terms
- Behavior logs
- Records of people's behavior, such as purchase histories and access logs
- Collaborative filtering
- An approach to information recommendation that suggests items a person is likely to like, based on records of users' behavior
- Value estimation
- Estimating the value of things from data, such as predicting housing prices or rents
ResearchResearch themes
The lab aims to discover useful insights from data and put them to use. Combining statistics, computer science, machine learning, and computational intelligence with real-world domain knowledge, it studies techniques for collecting and analyzing behavior logs, as well as their applications.
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MembersFaculty
The members of the Okamoto Lab consist of faculty, research collaborators, graduate students, undergraduate students, and exchange students.As of 2026
- Associate ProfessorKazushi Okamoto
- Assistant ProfessorKoki Karube
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: “Shopping online with bank card” Bogdan Hoyaux / European Commission / CC BY 4.0 (cropped)
- Recommender Systems and Value Estimation: “Large interior view of Plaza Singapura Shopping mall Orchard Road Singapore” Basile Morin / CC BY-SA 4.0 (cropped)
- Related lab (植野 真臣 研究室): “Bayesian network” Sachs K, Gifford D, Jaakkola T, Sorger P, Lauffenburger DA. / CC BY-SA 4.0 (cropped)
- Related lab (長濱 章仁 研究室): “Autonomous Delivery Vehicle Pileup” Mbrickn / CC BY-SA 4.0 (cropped)
- Related lab (原 聡 研究室): “Client Project Hashcat Testing With an AMD Epyc Supermicro Nvidia GPU Server Using Proxmox (Lawrence Systems) 07” Lawrence Systems / CC BY 3.0 (cropped)
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