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Fukunaga Lab
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An artificial intelligence lab that builds systems that look ahead before they act.
AboutAbout the lab
Systems that look ahead and act
The Fukunaga Lab studies systems that act while looking ahead.
Its targets include mechanisms by which autonomous agents plan their own actions, and intelligent systems that automatically solve complex combinatorial optimization problems.
Its fundamental research centers on implementing algorithms that can actually be put to practical use.
The lab also works on using machine learning to improve the performance of such algorithms, and on action planning with large language models.
Key terms
- Autonomous agent
- Software or a robot that assesses a situation and decides and acts on its own, without instructions from a person
- Combinatorial optimization
- The problem of finding the best combination from a vast number of possible selections or arrangements
- Large language model
- An AI that learns from large amounts of text and can understand and generate text. Also called an LLM
- Genetic algorithm
- A method that imitates biological evolution, searching for good solutions by crossing and mutating candidate solutions
ResearchResearch themes
The lab studies systems that act while looking ahead. With a focus on implementing algorithms, it researches decision-making and action planning by autonomous agents, as well as intelligent systems that automatically solve complex combinatorial optimization problems.
*Illustrative imageAction Planning and Combinatorial Optimization
The lab conducts fundamental research on decision-making and action planning by autonomous agents (such as planning problems) and on intelligent systems that automatically solve complex combinatorial optimization problems, centered on implementing practical algorithms.
*Illustrative imageMachine Learning, LLMs, and Action Planning
The lab researches how to use machine learning to improve algorithm performance. In addition to conventional search and action planning that use explicit models, it also studies action planning with large language models (LLMs), as well as action planning and search algorithms that use LLMs.
*Illustrative imageFrom Exact Search to Parallel Search
The lab covers a wide range of methods, from exact search such as graph search and branch and bound, to emergent methods such as local search and genetic algorithms. Its research ranges from algorithms that run on a single computer to parallel search using thousands of cores.
MembersFaculty
The members of the Fukunaga Lab are faculty, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorAlex Fukunaga
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: “Schachfiguren” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Action Planning and Combinatorial Optimization: “The ESA Seeker autonomous rover during tests at Paranal” ESA/RAL Space/ESO / CC BY 3.0 (cropped)
- Machine Learning, LLMs, and Action Planning: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- From Exact Search to Parallel Search: “Pleiades supercomputer racks 3” Oleg Alexandrov / CC BY-SA 4.0 (cropped)
- Related lab (Kaneko Lab): “Go (13×” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Related lab (University of Tokyo Baba Lab): “PVC Brainstorming Workshop ( )” BLM Nevada / Public domain (cropped)
- Related lab (Morihata Lab): “CSS code on a screen (Unsplash)” Sai Kiran Anagani _imkiran / CC0 (cropped)
- Related lab (Dobata Lab): “Pristomyrmex punctatus ( )” harum.koh from Kobe city, Japan / CC BY-SA 2.0 (cropped)
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