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research

I am broadly interested in the digital economy, and machine learning, large language models, and causal inference for managerial problems.

My recent work focuses on the creator economy. The creator economy is an ecosystem where media and commerce platforms, content creators, and brands collaborate to create and capture value by engaging and monetizing users. My research is motivated by a central question: How do creators, brands, and platforms better engage and monetize users? It concerns the process from producing compelling content to captivate an audience, fostering communities to enhance engagement, to monetizing through advertising or commerce. I develop or use empirical methods in machine learning, large language models, experimentation, and econometrics to address these questions by analyzing large-scale multimodal data.

selected publications

Engagement that Sells
Jeremy Yang, Juanjuan Zhang, and Yuhan Zhang

Marketing Science (2024) [paper] [code] [slides] [talk] [media]

- IP Registered under MIT License for Commercialization (2024/7)
- JMS China Annual Conference Best Paper Award (2021/11)
- MSI Alden G. Clayton Doctoral Dissertation Award (2021/5)
- MIT Sloan Doctoral Research Forum Thesis Prize (2021/4)

Targeting for Long-Term Outcomes
Jeremy Yang, Dean Eckles, Paramveer Dhillon, and Sinan Aral

Management Science (2023) [paper] [code] [slides] [talk] [media]

- INFORMS ISMS Frank M. Bass Dissertation Paper Award (2025/6)
- Finalist, INFORMS ISMS John D. C. Little Best Paper Award (2025/6)
- American Statistical Association Dissertation Award (2021/1)
- INFORMS Annual Meeting Best Paper Award (2020/11)
- Accepted for Presentation at the NeurIPS Conference (2019/12)

Interdependence and the Cost of Uncoordinated Responses
to COVID-19
David Holtz, Michael Zhao, Seth G. Benzell, Cathy Y. Cao, M. Amin Rahimiana, Jeremy Yang, Jennifer Allen, Avinash Collis, Alex Moehring, Tara Sowrirajan, Dipayan Ghosha, Yunhao Zhang, Paramveer S. Dhillon, Christos Nicolaides, Dean Eckles, and Sinan Aral

Proceedings of the National Academy of Sciences (2020) [paper] [code]

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