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Research Summary

We are advancing research in the field of computational social science, aiming to systematically understand phenomena related to business, society, and science and technology using artificial intelligence and data science approaches, and to generate new insights. We are also engaged in fundamental research on the methodologies themselves, focusing on techniques such as machine learning, deep learning, natural language processing, and large language models (LLMs).
Our analysis covers a wide range of data sources, including academic big data, social media, corporate transaction and M&A data, patent data, and urban behavioral data. Regarding generative AI, we are also pioneering efforts in the analysis of LLMs using inverse learning, as well as the analysis of social and scientific data utilizing LLMs.

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Laboratory Website

https://www.sakatalab.t.u-tokyo.ac.jp/en/ 

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Biography

Mar. 1989
Graduated from School of Economics, The University of Tokyo (Bachelor course)
May. 1997
Graduated from School of International Economics and Finance, Brandeis University (Master course)
Nov. 2003
Earned Doctor of Philosophy (Engineering), The University of Tokyo
July. 2013 - Present
Professor, Department of Technology Management for Innovation, Graduate School of engineering, The University of Tokyo
Apr. 2014
Director, Policy alternatives Research Institute, The University of Tokyo
Apr. 2015
Special Advisor to the President, The University of Tokyo
Apr. 2016
Head of the Department, Department of Technology Management, Graduate School of Engineering
Apr. 2020
Vice President, The University of Tokyo
Apr. 2021
Special Assistant to the President, The University of Tokyo
Apr. 2022 - Present
Director, Collaborative Research Organization for Future Regional Society, The University of Tokyo
Apr. 2026 - Present
Executive Director and Vice President, The University of Tokyo
Adviser to the Dean, Graduate School of Engineering

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Major Awards

• The 31st Annual Meeting of NLP (2025), Excellence Award, ELYZA Award, and SB Intuitions Award
• The 37th Annual Meeting of JSAI (2023), Excellence Award
• The 29th Annual Meeting of NLP (2023), Excellence Award
• Arakawa City Award for Distinguished Service (2021)
• AAAI 2019 Spring symposia, Special Paper Award from Reviewers

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Selected Publications

• Y. Long, L. Huang, S. Montagna, Z. Chen, X. Ding, Y. Shigetomi, T. Zhao, K. Asatani, I. Sakata, Y. Yoshida, "Tracing the evolution of household carbon emission research by machine learning", Environmental Impact Assessment Review 115 (2025), 108039.
  
• N. Higashide, T. Miura, Y. Tomokiyo, K. Asatani, I. Sakata, "Mid-career pitfall of consecutive success in science", Scientific Reports 14(1) (2024), 28172.　
  
• N. Higashide, K. Asatani, I. Sakata, "Quantifying advances from basic research to applied research in material science", Technovation 135 (2024), 103050.　
  
• T. Miura, K. Asatani, I. Sakata, "Revisiting the Uniformity and Inconsistency of Slow-cited Papers in Science", Journal of Informetrics 17 (1) (2023), 101378.
  
• M. Sugiyama, I. Sakata, H. Shiroyama, H. Yoshikawa and T. Taniguchi, “Five years on from Fukushima”, Nature 531(2016) , pp.29-31.
• H. Lu, M. Isonuma, J. Mori, I. Sakata, "UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation", The 13^th International Conference on Learning Representations (ICLR 2025), in Singapore (Apr 24-28, 2025).