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Intelligent Computing Lab
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The lab explores new forms of computation and optimization, including how even rippling water can serve as a computer.
AboutAbout the lab
Even rippling water can become a computer
The Intelligent Computing Lab is led by Project Associate Professor Akashi and Lecturer Fujii (formerly the Yamamoto Lab).
Project Associate Professor Akashi studies information processing that uses the dynamics (motion) of various physical systems for computation, such as fluid ripples and the bodies of robots.
Lecturer Fujii studies the area where combinatorial optimization, machine learning, and game theory intersect.
Key terms
- Reservoir computing
- A method that feeds input into complex dynamics such as those of a rippling fluid, reads out the response with machine learning, and uses it for computation
- Chaos
- Behavior that follows fixed rules, yet in which tiny differences in the initial state lead to greatly different futures, making prediction difficult
- Combinatorial optimization
- The problem of choosing the best one from among the combinations of many options
ResearchResearch themes
The lab studies information processing based on physical dynamics, as well as the area where combinatorial optimization, machine learning, and game theory intersect.
*Illustrative imageComputing with physical motion
If you feed input into a fluid by making it ripple, observe its motion, and read it out with machine learning, you can use a wide range of dynamics for computation, including those of robot bodies, light, quantum systems, and chemical reactions (reservoir computing). The lab studies this kind of information processing.
*Illustrative imageGenerating true random numbers from mechanical motion
Random numbers generated by computers do not strictly have the property of being unpredictable. The lab studies how to generate true random numbers with mechanical mechanisms, using the chaotic properties of Anosov systems, which have long been studied in dynamical systems.
*Illustrative imageOptimization, machine learning, and game theory
The lab studies the area where combinatorial optimization, machine learning, and game theory intersect. Its particular interests include submodular maximization, active learning, optimal stopping theory, online learning, and correlated equilibrium.
MembersFaculty
The members of the Intelligent Computing Lab include faculty, PhD students, master's students, and undergraduates.
- Program-Specific Associate ProfessorNozomi Akashi
- LecturerKaito Fujii
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: “Water Dolphin” JJ Harrison (https://www.jjharrison.com.au/) / CC BY-SA 3.0 (cropped)
- Computing with physical motion: “Spherical Reflection of Waves with the focus as observed in the Ripple Tank Experiment” Psgs123xyz (talk) (Uploads). / CC0 (cropped)
- Generating true random numbers from mechanical motion: “Würfel” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Optimization, machine learning, and game theory: “Computer, server room, engineer Fortepan 76098” FOTO:Fortepan — ID 76098: Adományozó/Donor: Erdei Katalin. archive copy at the Wayback Machine / CC BY-SA 3.0 (cropped)
- Related lab (Mathematical Information Systems Lab): “Cosine Series Plus Noise” Fourier1789 / CC BY-SA 4.0 (cropped)
- Related lab (Discrete Mathematics Lab): “Graph example (Graph theory)” Römert / CC BY-SA 3.0 (cropped)
- Related lab (Computer Software Lab): “Source code in Javascript” Romainhk / CC BY-SA 3.0 (cropped)
- Related lab (Collective Intelligence Systems Lab): “DataAppliedWeb” Marksenizer / CC BY-SA 3.0 (cropped)
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