LAB LOGA page on LabLog, a review site (not the lab's official page)
Hara Lab
Machine-translated · View original
The lab conducts algorithm research on making machine learning reliable and easy to use.
AboutAbout the lab
Building trustworthy machine learning, starting from the algorithms.
The Hara Lab studies machine learning algorithms and their application to science and measurement.
Complex machine learning models are accurate and useful, but they have also been pointed out to have reliability problems, such as the reasons for their decisions being unclear and their learning unfair biases.
To address these challenges, the lab studies explainable AI, which explains the reasons for a model's decisions, as well as the fairness of decisions.
The lab also studies how to make machine learning easier to use, so that good models can be built even by people who are not seasoned experts.
Key terms
- Explainable AI
- Technology that makes a machine learning model explain the reasons for its decisions so that users can understand them
- Black box
- A state in which users cannot understand the reasons for a model's decisions. It is one of the challenges in putting complex models into practical use
- Bias
- Discriminatory skew inherent in data. Gender discrimination is one example, and a model can learn it as is
NewsLab news
Paper on quantum state tomography (lead author: a Project Assistant Professor) accepted to NeurIPS 2026
A master's student received the Student Encouragement Award at MIRU 2026
A Professor received the Meguro-kai Award
Paper on the consistency of principal component analysis and spectral clustering accepted to AISTATS 2026
ResearchResearch themes
The Hara Lab studies machine learning algorithms and their application to science and measurement. Its research covers the reliability of machine learning models, the usability of machine learning, and knowledge discovery from data.
*Illustrative imageExplaining AI Decisions
Complex models suffer from the "black box" problem, in which users cannot understand the reasons for their decisions. To alleviate this problem, the lab studies explanation techniques that make models explain the reasons behind their decisions.
*Illustrative imageModel Fairness
Machine learning models can even learn discriminatory biases inherent in data. Research is revealing that model owners may be able to hide the existence of such unfair biases.
*Illustrative imageMaking Machine Learning Easier to Use
Building a good model requires trial and error based on experience, intuition, and expertise. The lab aims to make machine learning easier to use so that good models can be built even by people who are not seasoned experts.
*Illustrative imageEliminating Pitfalls in Data Analysis
Using analysis tools blindly can lead to pitfalls such as overlooking important knowledge or discovering false knowledge. The lab is developing new data analysis techniques that eliminate these pitfalls.
MembersFaculty
The members of the Hara Lab consist of faculty, researchers, administrative assistants, and students in the doctoral program, the master's program, and the fourth-year undergraduate year.As of 2026
- ProfessorSatoshi Hara
- Project Assistant ProfessorTakahiro Kajisa
VoicesStudent voices
Ratings and reviews reflect the experiences of individual current or former students, not the lab's official views. Whether a lab has reviews, or how many, says nothing about the lab itself.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
Student ratings (0 students)
--
--
--
--
--
--
Student voices answer questions like these
- What's great about this lab?
- What did you learn after joining?
- What's the vibe, and what is the professor like?
- Who is this lab for?
- Where do graduates go?
Your experience can help younger students choose their lab. Your name won't be shown.
Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to Hara Lab
Ueno LabSchool of Informatics and Engineering, Class I (Informatics)Artificial intelligence / Machine learning / Data scienceNishiyama LabSchool of Informatics and Engineering, Class I (Informatics)Machine learning / Statistical science / Data science
Harada LabSchool of Informatics and Engineering, Class I (Informatics)Machine learning / data science / data mining / artificial i…
Kohjima LabSchool of Informatics and Engineering, Class I (Informatics)Machine learning, data analysisThe information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
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: “Client Project Hashcat Testing With an AMD Epyc Supermicro Nvidia GPU Server Using Proxmox (Lawrence Systems) 07” Lawrence Systems / CC BY 3.0 (cropped)
- Explaining AI Decisions: “Decision Tree Anatomy” Lapollard / CC BY-SA 4.0 (cropped)
- Model Fairness: “Balance scale IMGP9755” Nikodem Nijaki / CC BY-SA 3.0 (cropped)
- Making Machine Learning Easier to Use: “RingaScientist-4” Kerstin Göpfrich / CC BY-SA 4.0 (cropped)
- Eliminating Pitfalls in Data Analysis: “Data visualization process v1” Farcaster at English Wikipedia / CC BY-SA 3.0 (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 (原田 慧 研究室): “Real-time bus tracking control room in Lebanon” UrusHyby / CC0 (cropped)
- Related lab (幸島 匡宏 研究室): “Machine Learning cookies” Syced / CC0 (cropped)
About LabLog
LabLog is a lab review app for students only. Student members at each university interview students in labs and publish what they say. Student voices can only be read by students who sign up with a university email address. Operated by bestiee Inc.
Learn more about LabLog ↗Search engines and AI may reference the basic lab info and rating summaries on this page and the description of LabLog. Student voices are for student members only. Automated collection by programs (scraping, or input to or training of AI) is prohibited under Article 8 of the Terms of Service. If you need data for research or analysis, please contact us.