Shibo Yan(厳 世博)
厳 世博 · ゲン セイハク
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About Me
Based in Tokyo, with a background in automation engineering and systems work. Hands-on with server operations, AI tooling, and quantitative analysis — now researching AI's measurable impact in Japan's IT sector.
Currently working on empirical research targeting listed IT companies in Japan: panel data methods, event studies, Python-based workflows. From infrastructure setup to analysis and interpretation.
Technical background, research mindset — looking to bring both into an IT role.
Skills
| Skill | Level | Evidence |
|---|---|---|
| Data & Analysis | ||
| Statistical modeling | Proficient | MCM/ICM 2020 · Master's thesis |
| Panel regression | Proficient | Master's thesis (FE / RE / CRE) |
| Event study | Proficient | Master's thesis |
| SPSS · MATLAB | Working | MCM/ICM 2020 |
| Python | Learning | Research data processing |
| Accounting & Finance | ||
| Bookkeeping | Working | Bookkeeping Grade 2 (簿記2級) |
| Financial statement analysis · Management accounting · Corporate finance | Working | Graduate coursework |
| Languages | ||
| Chinese | Native | — |
| Japanese | Business | JLPT N1 |
| English | Working | CET-4 · 525 |
Projects
-
[01] IEEE Conference Paper · 2026 Co-first Author
A Regulatory Placebo: The Failure of Mandatory GenAI Labeling
Compares mandatory labeling of AI-generated content across five jurisdictions and argues it works as a regulatory placebo — hard to enforce, technically self-defeating, and weakly justified. Proposes content- and risk-based governance instead.
- GenAI Governance
- Watermarking
- Comparative Law
-
[02] Master's Thesis · Ongoing
AI Adoption & Financial Performance
Investigating whether AI-related disclosure in corporate annual reports reflects genuine operational transformation or strategic narrative — and how this distinction relates to financial performance in Japan's listed IT sector.
- Empirical Research
- Panel Data
- Corporate Finance
-
[03] MCM/ICM 2020 · Problem E
Plastic Waste Modeling
Built a multivariate nonlinear regression model combined with a grey prediction system to estimate global disposable plastic waste thresholds. Applied TOPSIS-based environmental carrying capacity evaluation across four major world regions, analyzing production and recycling data from 2005 to 2017.
- Mathematical Modeling
- Statistical Analysis
- Environmental Systems
Education
-
2025 — 2027
Meiji UniversityM.Acc.
School of Professional Accountancy · Accounting
Research: AI adoption and corporate financial performance in Japanese markets
progress --% -
2018 — 2022
Southwest UniversityB.Eng. + B.A.
Control Engineering + Japanese Studies
Get in Touch
- shibo_yan@outlook.jp
- ORCID
- 0009-0003-5447-7744
- Institution
- Meiji University · Graduate School of Professional Accountancy
- Location
- Surugadai Campus · Chiyoda-ku, Tokyo
- Status
- M.S. 2nd Year · Expected March 2027