AIP Network Lab

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AIP Acceleration Research

"AIP Acceleration Research" aims to maximize the research result as an AIP Network Laboratory by supporting newly-proposed research projects based on the excellent research results among the laboratory.

AIP Acceleration Research FY 2019

Learning Analytics Foundation for Sustainable Learner-Centered Education

Research Director

Atsushi Shimada Professor, Faculty of Information Science and Electrical Engineering, Kyushu University

Collaborators

Hideaki Uchiyama Associate Professor, Library, Kyushu University
Masanori Yamada Associate Professor, Faculty of Arts and Science, Kyushu University

Description

This research project constructs a learning analytics foundation for the purpose of supporting diversifying learning in the global information society. To realize sustainable learner-centered education, we will tackle three issues: 1) development of advanced learning analytics methodologies to support reflection and overcoming weaknesses, 2) realization of a new university library as a learning hub which supports cooperative learning, and 3) development of learning analytics tools to educate learning-data scientists.

Big Data Assimilation and AI Creating New Development in Real-time Weather Prediction

Research Director

Takemasa Miyoshi Team Leader, Center for Computational Science, RIKEN

Collaborators

Naonori Ueda Deputy Director/Team Leader, Center for Advanced Intelligence Project, RIKEN
Hirofumi Tomita Team Leader, Center for Computational Science, RIKEN
Yutaka Ishikawa Project Leader, Flagship 2020 Project, Center for Computational Science, RIKEN
Shinsuke Satoh Research Manager, Remote Sensing Lavoratory, Applied Electromagnetic Researcy Institute, National Institute of Information and Communications Technology
Tomoo Ushio Professor, Graduate School of Engineering, Osaka University
Kana Koike Specialist, Lifestyle Services Business Division, MTI Ltd.
Yasuhiko Nakada Project Manager, Grid Transformation & Integration Division, R&D Department, TEPCO Research Institute, Tokyo Electric Power Company Holdings

Description

Weather affects people's lives and socioeconomic activities. Based on the achievements of the CREST research so far, we will accelerate our research toward smart society that can fully utilize advanced weather prediction. We will tackle technical challenges toward real-world implementation of the "Big Data Assimilation" technology that we have been developing. In addition, we will explore new developments of weather prediction and AI researches by combining AI and computer model simulation. These will lead to innovative weather prediction for better Quality-of-Life (QoL) and advanced socioeconomic activities.

Microbiome-based Precision Medicine

Research Director

Takuji Yamada Associate Professor, Department of Life Science and Technology, Tokyo Institute of Technology

Collaborators

Shinichi Yachida Professor, Faculty of Medicine, Osaka University

Description

The purpose of this research project is to establish an innovative diagnostic and therapeutic approaches "Microbiome-based Precision Medicine" using information of the human intestinal environment. We aim to identify human intestinal microbiota and microbial genes/metabolites which may be involved in carcinogenesis. These clinical and biological information will be stored in our database. In addition, we develop machine learning model-based method, which can be used as a clinical diagnosis to screen pre- and/or early cancerous stages. We will also develop novel methods to analyze human intestinal environmental data to identify microbiome biomarkers used in the above-mentioned diagnostic model.

Discovering Deep Knowledge by Advanced Utlization of Latent Space.

Research Director

Kenji Yamanishi Professor, Graduate School of Information Science and Technology, The University of Tokyo

Collaborators

Ryo Asaoka Consultant Ophthalmologist, Seirei Hamamatsu General Hospital/Clinical Associate Professor, Seirei Christopher University

Description

In the bigdata era, it becomes increasingly important to discover deep knowledge behind data as data becomes more complex. We aim at developing new technologies for analyzing heterogeneous and dynamic bigdata by advanced utilization of latent space lying behind observable data. Our research is categorized into three items: (1) Learning representations of latent space, (2) Detecting changes of latent structures and (3) AI-based ophthalmology based on advanced utilization of latent space.