Faculty Research Profile
Seung Ryul Shin
학력
2022: Ph.D. in Engineering, Seoul National University
2017: M.S. in Engineering, Seoul National University
2014: B.S. in Engineering, UCLA
주요 경력
2023~present: Assistant Professor, UNIST
2022~2023: Post-Doctoral Associate, Cornell University
2019: Visiting Scholar, UC Berkeley
수상/학회/외부활동
Best Conference Paper Award, RCEA Future of Growth Conference (2021)
Global Ph.D. Fellowship, National Research Foundation (2017-2020)
Excellence Award, Management Innovation Research Paper Competition, KMAC (2020)
Global Competence Program, National Research Foundation (2019)
Best Paper of the Conference Award, Korea Society of Strategic Management (2018)
Best Paper, Academy of Management (2018)
Grand Prize, Management Innovation Research Paper Competition, KMAC (2017)
데이터 기반 경영공학 연구실
Data-driven Management Engineering Lab
데이터 기반 경영공학 연구실에서는 빅데이터 분석 알고리즘과 통계 모델링 기반 인과성 추론 기법을 개발 및 적용하여 산업 내 혁신생태계를 구성하는 요인간의 인과성을 규명합니다. 특히, 기업, 발명가/과학자 등의 개인, 그리고 정부의 정책적 개입이 산업 내 기술혁신에 미치는 영향과 그 메커니즘을 분석하여 기업의 기술혁신 전략과 정부 정책에 대한 함의를 제시합니다.
Data-driven Management Engineering Lab develops and applies big data analytic algorithms and causal inference techniques to explore causal links between various factors in the innovation ecosystem. Specifically, we provide implications for firm R&D managers and policymakers by empirically analyzing how firms, individuals, and government policies influence technological innovation.
Data-driven Management Engineering Lab develops and applies big data analytic algorithms and causal inference techniques to explore causal links between various factors in the innovation ecosystem. Specifically, we provide implications for firm R&D managers and policymakers by empirically analyzing how firms, individuals, and government policies influence technological innovation.
연구분야
혁신경제학, 기술경영, 데이터 사이언스, 인과추론 / Economics of Innovation, Technology Management, Data Science, Causal Inference
Economics of Innovation, Technology Management, Data Science, Causal Inference
연구 희망분야
인공지능/기계학습 적용 데이터 분석 / Artificial Intelligence/Machine Learning Application for Data Analytics
Artificial Intelligence/Machine Learning Application for Data Analytics
연구주제
지식 확산 및 활용 전략 / Knowledge Diffusion and Utilization
과학기술 사업화 전략 / Commercialization of Scientific Knowledge
계산적 사회과학 / Computational Social Science
통계 모델링 기반 인과성 추론 / Causal Inference Statistical Modeling
Shift-Share 도구변수 / Shift-Share Instrumental Variable
Knowledge Diffusion and Utilization
Commercialization of Scientific Knowledge
Computational Social Science
Causal Inference Statistical Modeling
Shift-Share Instrumental Variable
국가연구개발사업 기술 분류체계
국가과학기술표준분류
SC. 경제/경영 > SC07. 경영전략/윤리 > SC0701. 경영전략/혁신
논문
Balsmeier, B., Fleming, L., Marx, M., & Shin, S.R. (2025). Startups, Unicorns, and the Local Influx of Inventors. Review of Economics and Statistics
Shin, S.R., Lee, J., Jung, Y.R., & Hwang, J. (2022). The diffusion of scientific discoveries in government laboratories: The role of patents filed by government scientists. Research Policy
Park, G., Shin, S.R., & Choy, M. (2020). Early mover (dis)advantages and knowledge spillover effects on blockchain startups' funding and innovation performance, Journal of Business Research