Academic Position

• KAIST Business School, Korea Advanced Institute of Science and Technology

  • Associate Professor (with Tenure), March 2020 - Present 

  • Assistant Professor, September 2012 - February 2020

Education

• Judge Business School, University of Cambridge, Ph.D. in Management Science.

• Judge Business School, University of Cambridge, M.Phil. in Management Science.

• Imperial College London, B.Eng, M.Eng. 

Research Interests

• Behavioral operations management

• Judgmental forecasting, decision making

• Empirical operations management

Publications

• Park, Y. S., Y. S. Lee & E. Siemsen (2025) "Two heads are better than one: Task division and decision control in inventory planning." Production & Operations Management. accepted. 

• Song,, K. H., Y. S. Lee & M. S. Koo (2025) "Stock market reactions to supply chain disruptions and recovery from the 2022 Shanghai Lockdown." Asia-Pacific Journal of Financial Studies. accepted.  

• Khosrowabadi, N., K. Hoberg & Y. S. Lee (2025) "Guiding supervisors in AI-enabled forecasting: Understanding the impact of salience and detail on decision-making." International Journal of Forecasting. 41(2). 716-732. 

• Koo, M. S., Y. S. Lee & M. Seifert (2025) "Investigating laypeople's short-and long-term forecasts of COVID-19 infection cycles." International Journal of Forecasting. 41(2). 452-465. 

• Park, Y. S., J. Na & Y. S. Lee (2024) "Effect of customer concentration on firm's operating performance during the COVID-19 pandemic." International Journal of Production Research. 62(19). 7146-7166. 

• Kim, H. Y., Y. S. Lee and D. B. Jun. (2020) Individual vs. group: Advice taking in judgmental forecasting adjustments. Journal of Behavioral Decision Making. 33(3). 287-303.  

• Kim, H. Y., Y. S. Lee and D. B. Jun (2019) "The effect of relative performance feedback on judgmental forecasting accuracy." Management Decision. 57(7). 1695-1711. 

• Lee, Y. S., D. Ribbink and S. Eckerd (2018) "Effectiveness of bonus and penalty incentive contracts in supply chain exchanges: Does national culture matter?" Journal of Operations Management. 62. 59-74. 

• Lee, Y. S., Y. W. Seo and E. Siemsen (2018) "Running behavioral operations experiments using Amazon's Mechanical Turk." Production & Operations Management. 27(5). 973-989.

• Kim, H., Y. S. Lee and K. S. Park (2018) "The psychology of queuing for self-service: Reciprocity and social pressure." Administrative Sciences. 8(4). 75. 

• Kang, H., B. W. Kim and Y. S. Lee (2017) "Supplier's corporate ability and consumer evaluation of a manufacturer." International Journal of Services and Operations Management. 27(1). 19-34.

• Lee, Y. S. and E. Siemsen (2017) "Task decomposition and newsvendor decision making." Management Science. 63(10). 3225-3245. 

• Lee, Y. S. (2014) "Management of a periodic-review inventory system using Bayesian Model Averaging when new marketing efforts are made." International Journal of Production Economics. 158. 278-289.

• Lee, Y. S. (2014) "A semi-parametric approach for estimating critical fractiles under autocorrelated demand." European Journal of Operational Research. 234(1). 163-173. 

• Lee, Y. S. and S. Scholtes (2014) "Empirical prediction intervals revisited." International Journal of Forecasting. 30(2). 217-234. 

Domestic

• 구문수, 이윤신, 박영수 (2023) "국가 간 무역분쟁에 따른 주식시장의 반응: 한일 무역분쟁 사례를 중심으로." 학국생산관리학회지. 34(2). 243-257. 

• 정동기, 이윤신, 김효영 (2018) "집단 내 개인의 영향력 및 책임감이 주관적 예측 조정에 미치는 영향." 한국생산관리학회지. 29(3). 299-329. 

Others

• Lee, J.Y., K. Kim, Y.S. Lee, H.Y. Kim, E. J. Nam, S. Kim, S. W. Kim and J. W. Kim (2017) "Treatment preferences for routine lymphadenectomy versus no lymphadenectomy in early-stage endometrial cancer." Annals of Surgical Oncology. 24(5). 1336-1342. 

• Lee, J.Y., K. Kim, Y.S. Lee, H.Y. Kim, E. J. Nam, S. Kim, S. W. Kim and J. W. Kim (2016) "Treatment preferences of advanced ovarian cancer patients for adding bevacizumab to first-line therapy." Gynecologic Oncology. 143(3). 622-627.

Working Papers

• "Aggregating Judgmental Demand Forecasts in Environments with Structural Breaks" with Matthias Seifert and Shijith Kumar (under revision, Decision Analysis)

• "False Confidence: The Pitfall of Using Precise Numbers in Forecasting Judgments" with Moon Su Koo and Chan Jean Lee (under review, Applied Cognitive Psychology)

• "A Behavioral Study of Inventory Allocation in Integrated Supply Chain" with Young Soo Park and Joo Hyung Park (under review, EJOR) 

• "Enhancing Chatbot Resue Intention: The Role of Last Impression, Apology and Error Attribution" with Ji-in Kim and Chan Hee Park

Teaching

• Operations Strategy and Supply Chain Management (MBA), KAIST Business School, Fall 2012 ~

• Service Management (MBA), KAIST Business School, Spring 2012 ~

• Quantitative Management (MBA), KAIST Business School, Fall 2014 ~

• Management Statistical Analysis (MBA), KAIST Business School, Spring 2018 ~

• Business Analytics (MBA), KAIST Business School, Spring 2019 ~

• Behavioral Operations Management (MS/PhD), KAIST Business School, Fall 2013 ~

Awards and Fellowships

• 우수 교육상, 카이스트 경영대학, 2024. 

• 카이스트 Q-Day  교원 특별표창 국제화 부분, 2024.

• 우수 교육상, 카이스트 경영대학, 2019. 

• 유민 이상문 신진생산관리학자상, 생산관리학회, 2019.

• 개교기념표창 학술상, 카이스트, 2019.

• 우수연구상, 카이스트 경영대학, 2018.

• Economic and Social Research Council (ESRC) Fellowship, 2011-2012.

• Finalist, INFORMS Case Competition, 2010. 

• Mellon Sawyer Dissertation Fellowship, 2009-2010. 

• Laing O'Rourke Doctoral Scholarship, 2007-2010.

• Governor's Prize, Imperial College London, 2006. 

Students

• 정동기 (MS) 피나클자산운용

• 김한영 (MS) NICE신용평가

• 이다빈 (MS) EY한영

• 정하준 (MS)  전문연구요원

• 송경희 (MS) University of Wisconsin-Madison 박사 과정 

• 김지인 (MS) 해성디에스  

• 조준영 (current MS) 

• 김효영 (PhD) 한국공학대학교 인공지능학과 조교수

• 박영수 (PhD) 국민대학교 경영학과 조교수 

• 구문수 (PhD) 삼성전자 메모리사업부 데이터 사이언스 파트 

• 박찬희 (current PhD)

• Rachel Tyree (current PhD) 

• 박주형 (current PhD)

연구 소개

For more information, send an email to yunshin@kaist.ac.kr

Empirical Operations Management

This is the field of operations management that applies empirical research methods. It involves collecting and analyzing empirical data (either collected from an industry or database) to gain insights into the various aspects of operation and service processes. Here, the aim is to develop a regression model to understand the relationships between different variables and make evidence-based decisions to improve operation and service performance. It can help bridge the gap between theory and practice by providing empirical evidence based on data from the real world. 

Examples of Current Projects

• Using wearables and machine learning algorithms to predict blood pressure: We collect smartwatch data, including lifestyle factors. Our goal is to develop machine learning algorithms to improve blood pressure prediction and to provide personalized recommendations for improving blood pressure. 

• Improving user retention in an exercise app: We collect physical activity data from an exercise app. Our goal is to examine how push notification design and incentive structures influence user physical activity and retention. 

Behavioral Operations Management

This research area is a subset of empirical operations management that focuses on understanding and influencing the behavior of individuals and groups involved in operation and service processes. It examines behavioral aspects such as decision-making, motivation, teamwork and coordination. We begin by formulating models and hypotheses about stakeholder behavior and test them through field and laboratory experiments. Our goal is to develop strategies to improve operations and service performance and decision-making processes. 

Examples of Current Projects

• Improving decision making with AI advice: We conduct lab experiments to examine how the format of AI advice, given as a point estimates vs. interval estimates, affects decision maker's reliance on the advice in estimation and prediction tasks.

• Investigating inventory allocation behavior in supply chain: We examine behavioral biases and social preferences in inventory allocation decisions using a lab experiment simulating an integrated supply chain with centralized decision authority. 

Photos