About Me
I study the economic complements needed for firms to realize productivity gains from machine learning and artificial intelligence. These complements include data, human capital & skills, organizational processes, and business models.
I am a first-year PhD student in the Technology & Operations Management (TOM) unit at Harvard Business School. I am advised by Iavor Bojinov and Hima Lakkaraju. I am a member of Hima’s AI4LIFE research group and Iav’s Data Science Operations Lab, both a part of Harvard’s D^3 Initiative.
My research interests lie in the broad areas of explainable ML, digital transformation, and data science operations. I work on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular, I am studying how domain experts use model explanations to reconcile their subject-area expertise with the predictions of machine learning systems. I also study how firms are using data science and machine learning to transform their business operations. This includes the following questions:
- How can management drive adoption of data science? How can firms build internal trust of machine learning systems?
- How should firms structure data lakes and data warehouses? How does data governance affect the success of data science initiatives?
- How do firms stay up-to-date on new capabilities in machine learning? How is knowledge related to data science and machine learning shared throughout organizations? I am particularly insipred by the concept of absorptive capacity.