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Harada-Kurose-Mukuta Lab (Machine Intelligence Lab/MIL)
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The lab builds intelligent systems that understand the real world and generate content.
AboutAbout the lab
Toward advanced intelligent systems that understand the real world
The Harada-Kurose-Mukuta Lab conducts research that extracts useful information from the real world and combines it with the vast data and computational power of cyberspace.
Its goal is advanced intelligent systems capable of understanding the real world, generating content, and discovering knowledge.
The lab carries out its research using computer science in general, including mathematical foundations and robotics.
Its research targets include image recognition, 3D information processing, research combining images and language, and intelligent robots.
Key terms
- Computer Vision
- Technology that enables computers to understand the objects and scenes captured in camera images and video
- Multimodal
- Handling information of different types, such as images, sound, and language, in combination
- Neural Network
- A computational mechanism that mimics the connections between neurons in the brain. It learns features from many examples
NewsLab news
Paper introduced: training a competitive game AI without lookahead search
Developed a large-scale vision-language model for medicine specialized in Japanese
Paper accepted at the international conference ECCV 2026
Paper accepted at the international conference IROS 2026
Paper accepted at the international conference ICML 2026
ResearchResearch themes
The lab studies machine learning and computer vision, centered on three pillars: processing and understanding real-world information, content generation, and intelligent robots. It takes on a wide range of topics, including 3D reconstruction, multimodal research dealing with images and language, medical image analysis, reinforcement learning, and causal inference.
*Illustrative image3D Reconstruction
Research on recreating the three-dimensional shapes of objects and environments on a computer from camera images. The lab works on reconstruction that includes changes over time, and has realized a method that automatically estimates the skeletons and joints of people and animals so that they can be moved freely.
*Illustrative imageAI That Answers Questions About Images
Research on visual question answering (VQA), the task of deriving the correct answer from an image and a question about it. Focusing on how information from the image and the text is fused, the lab proposed a method that works not only on photographs but also on illustrated images.
*Illustrative imageMedical Image Analysis
Research on using machine learning to detect and classify lesions in medical images taken during examinations. The lab participates in a project on the medical imaging big data platform of the National Institute of Informatics, and works on tasks such as analyzing very large pathology images.
*Illustrative imageComputation on Brain-Inspired Neuromorphic Chips
A method for computing neural networks using only on/off signals on neuromorphic chips that mimic the biological brain. Since it is expected to enable recognition with little computation, the lab proposed a method for generating images with this approach.
MembersFaculty
The members of the Harada-Kurose-Mukuta Lab consist of faculty, project researchers, visiting researchers, and doctoral, master's, and undergraduate students.
- ProfessorTatsuya HaradaPublications ↗
- LecturerYusuke MukutaPublications ↗
- Project LecturerYusuke KurosePublications ↗
- Assistant ProfessorWestfectel Thomas
- Project Assistant ProfessorYasuhiro Kato
- Project Assistant ProfessorQianyu Zhou
- Project Assistant ProfessorZiteng Cui
- Project Assistant ProfessorDexuan Zhang
- Project Assistant ProfessorNabarun Goswami
- Project Assistant ProfessorYuan Gan
- Project Assistant ProfessorHaozhi Cao
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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: “Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco” Daniel L. Lu / CC BY 4.0 (cropped)
- 3D Reconstruction: “Monmouth castle point cloud, created with Photosynth 01” John Cummings / CC BY-SA 3.0 (cropped)
- AI That Answers Questions About Images: “Ceratotherium-simum-simum-combined-overlay” MartinThoma / CC0 (cropped)
- Medical Image Analysis: “Part of whole slide image of a Wilms' tumor of the kidney” Mikael Häggström, M.D. / Public domain (cropped)
- Computation on Brain-Inspired Neuromorphic Chips: “ASNN” Daemon2010 / CC BY-SA 4.0 (cropped)
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（深尾研究室） ): “Autonomous compact tractors in a Texas vineyard, Nov 2012” ASIrobots / CC BY-SA 3.0 (cropped) - Related lab (JSK研究室
（情報システム工学研究室／Jouhou System Kougaku Laboratory） （岡田研究室） ): “Ars Electronica 2008 Kotaro” Manfred Werner - Tsui / CC BY-SA 3.0 (cropped) - Related lab (Cyber Interface Lab
（葛岡・谷川・鳴海研究室） ): “CAVE Crayoland” User:Davepape / Public domain (cropped)
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