Research
Our laboratory aims to understand phenomena in business, society, and science & technology using data-science methodologies, generating new insights across these domains. We focus on machine learning, deep learning, natural language processing, and network analysis, while also pursuing foundational research on these methods.
Our analyses cover diverse datasets including large-scale scholarly corpora, social media, corporate transaction and M&A information, patent data, urban mobility datasets, and review texts. We are also among the first to analyze large language models (LLMs) via inverse learning and to utilize LLMs in the analysis of scientific and social systems.
Students are encouraged to choose their research topics autonomously, leading to a rich variety of themes. Active collaboration across domains is a defining characteristic of our research environment.
We also collaborate with external organizations such as the Technology Informatics Social Collaboration Program and the NEDO/AIST Science-Technology Trend Forecasting Project, emphasizing societal impact and engagement with broader research communities.
Science of Science
We analyze how scientific knowledge and technological innovation emerge and diffuse through papers, patents, and corporate activities. Social factors such as diversity and gender are also key themes in this rapidly growing field.
Recent Research Highlights
- Measuring global advancement and delay of research topics
- Modeling science ecosystems centered on fundamental researchers
- Understanding mechanisms of policy citations to scientific papers
- Analyzing leading researchers in the circular economy domain
Computational Social Science
We analyze human behavior and online interactions using large-scale data. Our work includes Google Maps and Twitter (X) analysis, LLM-based social data analytics, and studies on techno-economic interactions using firm networks and patent information.
Recent Research Highlights
- Tracking structural changes in political communities on Twitter
- Understanding urban interaction patterns using Google Maps data
- Analyzing scientists’ social media use and research impact
- Distance characteristics in regional-bank-centered transaction networks
- Evaluating corporate portfolio strategies using patent-space models
Natural Language Processing
We develop advanced methods such as inverse learning, model composition, and training-data distillation to understand how language models acquire generalization and reasoning abilities. We also pursue improved transparency, safety, and efficiency, while exploring new frameworks that use LLMs as autonomous agents.
Recent Research Highlights
- Analyzing training data of large language models via inverse learning
Research Highlights
Quantifying progress in research topics across nations
Quantitative time-series comparison of research topics across nations
Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers
Analyzing how sustainability literature addresses the SDGs
Large-scale analysis of delayed recognition using sleeping beauty and the prince
長い間注目されなかった論文であるSleeping Beautyとそのきっかけとなる論文であるPrinceを大規模に取得し、その特徴を明らかにしました。非連続な発見は遠い分野間だけではなく、近くの分野の見落としにも多く存在していることがわかりました。
Evaluating Nodes of Latent Mediators in Heterogeneous Communities
異質なコミュニティをつなぐ結節点(ノード)を検出する新しいネットワーク指標(PW)を提案
Dense and influential core promotion of daily viral information spread in political echo chambers
Twitterの政治的エコーチャンバーにおいて効率的な情報拡散が行われている過程を明らかにしました。
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
木構造上のトピックから各トピックに関する要約文を生成する教師なし要約手法を提案