Dokyun Lee

Dokyun (DK) Lee studies the {responsible application, development, impact} of AI in digital consumer and e-commerce analytics with a focus on text data. Specific interests are:

• Applying machine learning techniques along with causal inference methods to quantify the economic impact of unstructured data (e.g., text, images),
• Content extraction, understanding, and engineering,
• Application and Impact of Generative AI in the context of social media, advertising, user-generated data, digital consumer management, innovation & creativity, and patents.

His work has been published in journals and conferences including Management Science, Information Systems Research, MISQ, Journal of Marketing Research, AAAI, AIES, and WWW. He is a recipient of the ISS Gordon B Davis Young Scholar, 2021 Marketing Science Institute Young Scholar, CDO Magazine Academic Data Leader, ISR Best Paper Award, MSBA Teaching Award, Management Science Distinguished Service Award, and Best Conference Paper Awards. His work is supported by organizations such as Adobe, Bosch Institute, Google Cloud, Marketing Science Institute, McKinsey & Co, Nvidia, and Net Institute.

DK holds a Bachelor’s degree in Computer Science from Columbia University (Machine Learning Focus), a Master’s degree in Statistics (Master’s Thesis: Johnson-Lindenstrauss Lemma and its Effect on Supervised Learning) from Yale University, and a Ph.D. from the Operation, Information, and Decisions department of the Wharton School (Thesis: Three Essays in Big Data Consumer Analytics in E-Commerce). Before academia, DK worked at 4 tech start-ups and Blackrock as a quantitative software engineer and at Thomson Reuters as an ML contractor building a natural language processing engine for financial data.

Education

• Phd, The Wharton School, 2015
• MA, Yale University, 2010
• BA, Columbia University, 2009

Positions Held

• 2021- Kelli Questrom Associate Professor in IS (with Tenure)
  Secondary: Computing and Data Sciences
  Digital Business Fellow at Digital Business Institute
  Computing & Data Sciences Faculty Fellow
  Questrom School of Business
  Computing & Data Sciences
  Boston University, MA
• 2015- 2021 Assistant Professor of Business Analytics (Tenure-track)
  Xerox Junior Faculty Chair 2017-2018
  Carnegie Mellon University, PA
• 2020 - Founding Member & Co-Director of EnAIble (CMU AI Retail & Service Collaborative)
• 2010 Machine Learning and Natural Language Processing Engineer Contractor
  Thomson Reuters, New York, NY
• 2007-2008 J2ME Mobile Widget Software Engineer Snac Inc., New York, NY
• 2007 Summer Quantitative Trading Software Developer
  Blackrock, New York, NY
• Since 2006 Four Different Start-ups as a Machine Learning Engineer or Software Engineer

Selected Publications

• Lee, D., Anandasivam, G., Lee, D., Dongwook, S. (In Press). "Nudging Private Ryan: Mobile Micro-giving under Economic Incentives and Audience Effects", MIS Quarterly
• Manzoor, E., Chen, G., Lee, D., Smith, M. (In Press). "Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online", Management Science
• Zhang, S., Lee, D., Singh, P., Srinivasan, K. (In Press). "How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics", SSRN Electronic Journal
• Hsu, P., Lee, D., Tambe, P., Hsu, D. (In Press). "Deep Learning, Text, and Patent Valuation", SSRN Electronic Journal
• Ahn, D., Lee, D., Hosanagar, K. (In Press). "Interpretable Deep Learning Approach to Churn Management", SSRN Electronic Journal
• Burtch, G., He, Q., Hong, Y., Lee, D. (In Press). "Peer Awards Retain New Users and Encourage Exploitation in Users’ Production of Creative UGC", SSRN Electronic Journal
• Lee, D., Hosanagar, K. (In Press). "How Do Product Attributes Moderate the Impact of Recommender Systems?", SSRN Electronic Journal
• Shi, Z., Liu, X., Lee, D., Srinivasan, K. (2023). "How Do Fast-Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media", Journal of Marketing Research
• Zhang, S., Lee, D., Singh, P., Mukhopadhyay, T. (2021). "EXPRESS: Demand Interactions in Sharing Economies: Evidence from a Natural Experiment Involving Airbnb and Uber/Lyft", Journal of Marketing Research 002224372110621-002224372110621
• Wang, W., Zhao, H., Lee, D., Chen, G. (2021). "Machine Learning for Consumers and Markets", Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• Burtch, G., He, Q., Hong, Y., Lee, D. (2021). "How Do Peer Awards Motivate Creative Content? Experimental Evidence from Reddit", Management Science
• Lee, D., Hosanagar, K. (2021). "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?", Management Science, 67 (1), 524-546
• Proserpio, D., Hauser, J., Liu, X., Amano, T., Burnap, A., Guo, T., Lee, D., Lewis, R., Misra, K., Schwarz, E., Timoshenko, A., Xu, L., Yoganarasimhan, H. (2020). "Soul and machine (learning)", Marketing Letters, 31 (4), 393-404
• Liu, X., Lee, D., Srinivasan, K. (2019). "Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning", Journal of Marketing Research, 56 (6), 918-943
• Lee, D., Hosanagar, K. (2019). "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment", Information Systems Research, 30 (1), 239-259
• Burtch, D. (2019). "Peer Symbolic Awards Increase User Content Generation but Reduce Content Novelty",
• Lee, D., Hosanagar, K., Nair, H. (2018). "Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook", Management Science, 64 (11), 5105-5131
• Lee, D., Gopal, A., Lee, D. (2018). "Micro-Giving: On the Use of Mobile Devices and Monetary Subsidies in Charitable Giving", Academy of Management Proceedings, 2018 (1), 15011-15011
• Lee, D., Hosanagar, K. (2016). "When do Recommender Systems Work the Best?", Proceedings of the 25th International Conference on World Wide Web
• Hosanagar, K., Fleder, D., Lee, D., Buja, A. (2014). "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation", Management Science, 60 (4), 805-823

Selected Research Presentations

• Lee, D. Invited University Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, Nova SBE Lisbon, 2023
• Fournier, S. , Jing, C. , Lee, D. , Srinivasan, S. Managing and Mitigating Firm Socio-Political Risk Events, Marketing Strategy Meets Wall Street, Chicago, 2023
• Lee, D. Invited University Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, Tel Aviv University, 2023
• Lee, D. Invited University Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, Temple University, 2023
• Lee, D. Digital Marketing Symposium: Relevance and Privacy- Leveraging Personalized Marketing for Responsible Growth, Questrom School, 2023
• Lee, d. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, Nanyang Technological University, 2023
• Lee, D. DBI + Susilo Institute Symposium: The Emerging Impact of AI on Consumer Engagement, Questrom School, 2023
• Lee, D. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, American University, 2023
• Cheng, Z. , Jin, G. , Leccese, M. , Lee, D. , Wagman, L. Startup Buyout and Patentable Innovation: New Evidence from PatentsView, AEA, 2023
• Kim, K. , Lee, D. , McCarthy, D. Deep Learning Methods for Customer Base Analysis: Evidence from 1000 companies over 6 years, WISE, Denmark, 2022
• Hui, X. , Lee, D. , Zhou, E. Virtue Signaling Via Image in Second-Hand Markets: Evidence from the GPU Market, WISE, Denmark, 2022
• Lee, D. Innovation Information Initiative Technical Working Group Meeting, NBER, 2022
• Fournier, S. , Jing, C. , Lee, D. , Srinivasan, S. Managing and Mitigating Sociopolitical Firm Risk Events, Marketing Dynamics Conference, Georgia State University, 2022
• Kim, K. , Lee, D. , McCarthy, D. Deep Learning Methods for Customer Base Analysis: Evidence from 1000 companies over 6 years, MARKETING DYNAMICS CONFERENCE, 2022
• Lee, D. Human Flourishing and the futures of Intelligence, Stanford, 2022
• Lee, D. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, University of Illinois Chigago, 2022
• Kim, K. , Lee, D. , McCarthy, D. Deep Learning Methods for Customer Base Analysis: Evidence from 1000 companies over 6 years, Marketing Strategy Meets Wall Street Conference, 2022
• Lee, D. The future of customer analytics, driven by AI, Meta NYC, 2022
• Cheng, Z. , Lee, D. , Tambe, S. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, NUS, Singapore, 2022
• Cheng, Z. , Lee, D. , Tambe, S. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, The Chinese University of Hong Kong, 2022
• Cheng, Z. , Lee, D. , Tambe, S. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, University of Rochester, 2022
• Cheng, Z. , Lee, D. , Tambe, S. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, University of Florida, 2022
• Fournier, S. , Jing, C. , Lee, d. , Srinivasan, S. Managing and Mitigating Firm Risk Events, Questrom Data Blitz, Questrom, 2021
• Fournier, S. , Jing, C. , Lee, D. , Srinivasan, S. Managing and Mitigating Firm Risk Events, 2021 Conference on Artificial Intelligence, Machine Learning, and Business Analytics, Philadelphia, online, 2021
• Cheng, Z. , Lee, D. , Manzoor, E. University Invited Seminar: Focused Concept Miner, Georgia Institute of Technology, 2020

Awards And Honors

• 2022, Best Student Paper Award Finalist at WISE
• 2021, MSI Young Scholar 2021
• 2021, MSBA Faculty Excellent Award for Teaching
• 2021, Management Science ISR Division 2018-2020 Best Paper Award Finalist, Institute for Operations Research and the Management Sciences
• 2021, Best Paper in Psychology of Technology Award
• 2020, WISE 2020 Best Student Paper Award
• 2020, Best Student Paper Runner Up
• 2020, Best Paper Award In Information System Research 2021
• 2020, CDO Magazine 2021 List of Leading Academic Data Leaders
• 2020, ISS Inaugural Gordon B. Davis Young Scholar
• 2020, Management Science ISR Division 2017-2019 Best Paper Award Finalist, Institute for Operations Research and the Management Sciences
• 2019, INFORMS Best Paper in E-Business - Nominated, INFORMS
• 2019, Management Science Distinguished Service Award
• 2019, Management Science ISR Division 2016-2018 Best Paper Award Finalist, Institute for Operations Research and the Management Sciences
• 2018, INFORMS Best Paper Runner Up
• 2018, Academy of Management Best Student Paper Award
• 2017, ICIS 2017 Best Track Paper - IT and Social Change
• 2017, ICIS 2017 Best Conference Paper
• 2017, CIST Best Student Paper Award
• 2016, ISS Nunamaker-Chen Dissertation Award Runner Up
• 2016, CIST Best Student Paper Award, INFORMS
• 2016, The Lave Weil Faculty Research Award, Carnegie Mellon University
• 2016, Management Science ISR Division 2012-2014 Best Paper Award Finalist, Institute for Operations Research and the Management Sciences
• 2014, WISE 2014 Best Student Paper Award Runner Up