Daniel Yue
Doctoral Student
Doctoral Student
Daniel is a doctoral candidate in the Technology and Operations Management Unit at Harvard Business School. His research focuses on how firms leverage data analytic methodologies to create competitive advantage. In particular, he focuses on the use of two dominant statistical frameworks — experimentation and prediction (artificial intelligence) — and the organizational processes necessary to make them work. Before joining HBS, Daniel was a Product Manager at Mastercard (Applied Predictive Technologies), where he designed analytics software to guide businesses in conducting and analyzing field experiments on strategic initiatives. He graduated with an AB in Physics from Harvard College in 2016.
Daniel is a doctoral candidate in the Technology and Operations Management Unit at Harvard Business School. His research focuses on how firms leverage data analytic methodologies to create competitive advantage. In particular, he focuses on the use of two dominant statistical frameworks — experimentation and prediction (artificial intelligence) — and the organizational processes necessary to make them work. Before joining HBS, Daniel was a Product Manager at Mastercard (Applied Predictive Technologies), where he designed analytics software to guide businesses in conducting and analyzing field experiments on strategic initiatives. He graduated with an AB in Physics from Harvard College in 2016.
- Working Papers
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- Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. View Details
- Cases and Teaching Materials
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- Greenstein, Shane, Daniel Yue, Kerry Herman, and Sarah Gulick. "Hugging Face: Serving AI on a Platform." Harvard Business School Case 623-026, November 2022. (Revised January 2023.) View Details
- Area of Study
- Areas of Interest