Daniel Yue

Daniel Yue is an Assistant Professor in the Information Technology Management area at the Scheller College of Business, Georgia Institute of Technology. His research investigates the strategic dynamics of open innovation, particularly in artificial intelligence and open-source software ecosystems. He holds a Ph.D. in Business Administration from Harvard Business School (2024) and an A.B. in Physics from Harvard College (2016).

• Ph.D., Business Administration – Harvard Business School, 2024
• A.B., Physics – Harvard College, 2016

Dr. Yue's research centers on open disclosure—why firms share innovative knowledge without direct profit. His work uses AI research, scientific publications, and open-source software as empirical settings to develop and test theories on innovation, governance, and corporate involvement in science. He explores how corporate participation affects research quality, how tool access influences model development, and how open-source software shapes the trajectory of AI.

His recent publications examine high-impact topics such as the governance shift of PyTorch from Meta to a non-profit foundation, the citation benefits of corporate-affiliated AI research, and the economic value generated by machine learning open-source software. These studies reveal trends in technology control, researcher incentives, and software-driven innovation, positioning his work at the intersection of management, technology, and policy.

Although no formal scientific awards are listed, his papers are under review or revised at top-tier journals like Management Science, indicating strong scholarly recognition.

Daniel Yue advises students in IT management and innovation-related topics, though specific advisees are not listed. His research is supported by empirical data from GitHub, field experiments, and large-scale analysis of AI publications. He previously worked as a product manager of analytics software at Mastercard, bringing industry experience into his academic work.

He is actively involved in research teams studying open collaboration and AI governance, with collaborations including Frank Nagle, Paul Hamilton, Iavor Bojinov, and Max Langenkamp. His work contributes to both academic theory and practical implications for technology firms and policymakers.