At AIMS Lab, we conduct a wide range of research into theories and technologies for modeling, time series analysis/data analysis, control system design, system implementation, and decision-making using mathematical approaches based on systems control theory, optimization theory, signal processing, nonlinear time series analysis, multivariate analysis, machine learning/deep learning theory, etc. What are your aims?

Research fields

• Research on control technology for vehicle systems 

We are researching control technologies for improving the dynamic performance, safety, and riding comfort of vehicle systems such as automobiles, ships, airplanes, and mobile robots, as well as automation technologies for cooperative work using multiple mobile robots. We are also researching control technologies for the development of high-performance, energy-efficient next-generation electric vehicles, convoy formation control that applies biological analogies (such as bat flight models), and fundamental control technologies for smart cities and ITS that primarily use EVs. <This research is primarily led by Professor Kawabe.>

• Learning based control with world model

We are conducting research into the construction of highly accurate and generalizable world models for autonomous systems, and real-time, robust control methods based on these models. Specifically, we are building self-organizing world models that incorporate deep reinforcement learning, and developing model-predictive learning control methods based on interactions with the environment. <This research is primarily led by Professor Kawabe.>

• Theory and Applications of Nonlinear Time Series Analysis

We intend to reach social changes by deepening time series analysis/data analysis. Now, our students try to understand/forecast large earthquakes as well as distinguish Parkinson’s disease patients from healthy controls with their walking behavior. If we reconstruct the chromosomal shapes, we may be able to tell which organ a cell comes from and what disease a person suffers from. If you want to understand problems of the current machine learning/deep learning, and choose a third alternative path, our research group could best fit for you. Shall we make such social changes by analyzing data with a computer and mathematics? <This research is primarily led by Professor Hirata.>

• Research on Machine Learning and Mathematical Optimization for Optimal Decision-Making

We develop methods that integrate machine learning and mathematical optimization to enable optimal decision-making under uncertainty. In many real-world settings, decision-making follows a two-stage framework—predict then optimize: first, the future is predicted, and then actions are optimized based on that prediction. While this framework allows for optimal decisions when predictions are perfectly accurate, in practice, predictions inevitably contain errors, which can lead to suboptimal outcomes. To address this challenge, we focus on developing more robust prediction techniques and optimization methods. <This research is primarily led by Assistant Professor Ikeda.>

Advisory Staffs

• Professor
  Tohru Kawabe
  
  

• Professor
  Yoshito Hirata

• Assistant Professor
  Shunnosuke Ikeda

2025 Student Members 

• Graduate Students [Graduate School of Science and Technology]

Degree Programs in Systems and Information Engineering

     Doctoral program in Computer Science

Sayaka Nagasaka 

Saldanha Matheus Henrique Junqueirao

Md. Mehedi Hasan  

    Master's program in Computer Science

Ryo Isogawa

Taisei Eguchi

Yuuichi Kondo

Ryota Toji

Liu Zhuocheng

ZHANG YUNPENG 

DU KAI

• Undergraduate Students

    [College of Media Arts, Science and Technology ]

Hinano Kawakami

Riku Kirita

Hibiki Sugano

Naoki Fujiwara

Tomoki Mishima

Amane Watanabe

    [College of Information Sciences ]

Kei Kondo

Reo Nishikawa

awakami

Kazuto Watase

Affiliated laboratories:

• Frog Laboratory

• System Optimization Laboratory

Inquiries: 

If you would like to visit or have any questions, please contact

 (aims-info<AT>googlegroups.com).

Places of AIMS Lab.       Access Map

3F922(9th floor, Building F) 

3E107-1(1st floor , Building E)

All rooms are located in the central area of Tsukuba  Campus. The nearest bus stop is "Dai-San Area Mae".

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