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YU,  Jungju Assistant Professor 사진
YU, Jungju Assistant Professor
Contact Information
Research Areas Branding, Value of customer data, Targeted advertising, Customer data privacy, Customer journey
Jungju Yu is an Assistant Professor of KAIST College of Business and EWon Assistant Professor, a distinguished university faculty position KAIST. Before joining KAIST, he served as an assistant professor at City University of Hong Kong. He earned his Ph.D. from Yale School of Management and Sc.B. in mathematics from Brown University.

At KAIST, he teaches an MBA course in Business Analytics and a Master/Ph.D. seminar in Quantitative Models for Marketing Decisions.

His research primarily focuses on branding, digital targeted advertising, consumer data privacy, and customer journey. His early research works have been recognized in the marketing field. Notably, he was honored as a 2023 MSI (Marketing Science Institute) Young Scholar, an accolade awarded biennially to the most promising young marketing academics internationally. He has published his research in leading journals such as Marketing Science, Management Science, and the RAND Journal of Economics. Further highlighting his expertise, he serves as a reviewer on the editorial board of Marketing Science.

Outside the academic realm, he enjoys playing tennis and soccer, traveling, and indulging in classical music. CV_JY(0).pdf


    Yale University School of Management
    Ph.D. in Marketing, 2018

    Yale University School of Management
    M.Phil., M.A. in Marketing, 2015

    Brown University
    Sc.B. in Mathematics, 2012


    KAIST College of Business (Seoul Campus)
    - Assistant Professor (6/2021 -- present)
    - EWon Assistant Professor (3/2023 -- 2/2026)

    City University of Hong Kong
    - Assistant Professor of Marketing (8/2018 -- 6/2021)

Industry Advisory Activities

    Corporate consulting
    - Bringing new technology to the market, Developing branding strategy
    - Companies: LG Electronics, Virnect, Aqaralife, Zeiss Korea

    Corporate teaching
    - Business analytics, Data-driven marketing, Strategic brand management
    - Companies: SKT, SK mySUNI
Publications & Research

Publications (patents, etc.)

    [Published and Accepted Papers]

    1. Ke, Tony, Jiwoong Shin, and Jungju Yu (2023). "A Model of Product Portfolio Design: Guiding Consumer Search through Brand Positioning." Marketing Science, 42(6): 1101--1124.

    2. Despotakis, Stylianos, and Jungju Yu (2023). "Multidimensional Targeting and Consumer Response." Management Science, 69(8): 4363--4971.

    3. Shin, Jiwoong, and Jungju Yu (2021). "Targeted Advertising and Consumer Inference." Marketing Science, 40(5): 900--922.

    4. Yu, Jungju (2021). "A Model of Brand Architecture Choice: a House of Brands vs. a Branded House." Marketing Science, 40(1): 147--167.

    5. Neeman, Zvika, Aniko Ory, and Jungju Yu (2019). "The Benefit of Collective Reputation." The RAND Journal of Economics, 50(4): 787--821.

    [Work in progress]

    1. "Targeted Advertising: Strategic mistargeting and personal data opt-out," with Eddie Z. Ning and Jiwoong Shin.

    3. "Communicating attribute importance under competition," with Jae-Yoon Lee and Jiwoong Shin.

    2. "A mechanism of explainable AI," with Sungwoon Byun.

    [Awards, Honors and Grants]

    MSI 2023 Young Scholars (2023)

    EWon Assistant Professor (2023--2026)

    ISMS Early Career Camp Fellow (2022)

    Harry and Heesun You Fellowship (2017, Yale School of Management)

    AMA-Sheth Foundation Doctoral Consortium Fellow (2017)

    Yale Center for Customer Insights Fellow (2017)

    ISMS Doctoral Consortium Fellow (2014)

    [Academic Services]

    Editorial Review Board Member at Marketing Science (2022--present)

Research Areas

    Value of customer data, Data privacy, Digital advertising, Business implications of AI, Branding and firm reputation.

Business Analytics(BIZ581)

    The course is designed to equip you with tools to analyze a data set and draw important insights. Students learn both theory and concepts of major analytic tools in prediction and classification and their applications using real datasets. Students also learn how to make better business decisions as managers based on the insights drawn from data.

Quantitative Models for Marketing Decisions(BME641)

    This course provides research-oriented students with foundational knowledge in quantitative marketing, focusing on game-theoretic modeling.
Contact : Joo, Sunhee ( shjoo2006@business.kaist.ac.kr )

Faculty & Research