Members

Takashi Isozaki

We are now using more data than ever before, and this usage is sure to go on increasing in the future. Many examples of mass-data can be listed, such as data related to geoenvironmental assessments, gene expressions, and buying history. It is easy to imagine that success in utilizing such data would have a great influence on the future of humanity. However, humankind does not currently have the ability to fully extract information from data, which I believe is a major problem. I have addressed this problem by treating data that have many variables dependent on each other using probabilistic graphical models and methodologies of physics.

[Keywords]
Data Analysis / Statistical Inference / Causal Information Analysis / Open Systems-data Analytics

Activities

2024/11/12
'WORLD' by Researcher Takashi Isozaki is now available.
Challenging the Limits of Information Extraction from Data
2024/8/5
[Article]A paper by Isozaki et al has been presented at AAAI 2024's GCLR Workshop
Adjustment Variable Selection for Intervention Estimation Robust to Data Size
2024/3/5
2022/9/7
Sustainability Report 2022
2022/8/26
2021/11/4
2021/10/27
2020/4/21
2019/11/6
2019/4/23
New Publication
Book commemorates 30th anniversary of Sony CSL
2019/2/25
2019/1/15
Sony CSL 30th Anniversary Open House Report
October 23 -24, 2018
2018/11/22
2018/10/16
解析ツール「CALC（カルク）」に関する記事が掲載されました。
2017/12/14
［PRESS RELEASE］
Provision of CALC Big Data Analysis Services Utilizing AI
2017/5/30
ヒューマンキャピタル2016
2016/5/31
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Worlds

Challenging the Limits of Information Extraction from Data

Takashi Isozaki
2024/8/5

CALC

A big Data Analysis Tool
2019/2/22
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Selected Publications

Isozaki, T. and Kuroki, M. (2017), Learning Causal Graphs with Latent Confounders in Weak Faithfulness Violations, New Generation Computing, Vol. 35, Issue 1, pp. 29-45.

Funabashi, M., Hanappe, P., Isozaki, T., Maes, A.M., Sasaki, T., Steels, L. and Yoshida, K. (2015), Foundation of CS-DC e-laboratory: Open Systems Exploration for Ecosystems Leveraging, Proceedings of CS-DC 2015, Springer.

Isozaki, T. (2014), A Robust Causal Discovery Algorithm against Faithfulness Violation, Trans. of the Jpn. Soc. for AI, Vol. 29, No.1, pp. 137-147.

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Profile

After studying theoretical physics at the Department of Physics, Tokyo Institute of Technology, and through the masters program in physics at the Faculty of Science, Tohoku University, Isozaki worked at a precision instruments manufacturer, where he carried out research on optical and electronic devices before moving on to apply probabilistic models to artificial intelligence. After earning a PhD in engineering from the Graduate School of Information Systems at the University of Electro-Communications, he joined Sony Computer Science Laboratories, Inc. Isozaki is currently a senior researcher and doing basic research on statistical estimation methods and causal analysis using a physics approach, and the applications of these techniques to data analysis and AI. He also conducts research into Open Systems Data Analytics, exploring methodologies for the data analysis of open systems.

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