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:

  1. How can management drive adoption of data science? How can firms build internal trust of machine learning systems?
  2. How should firms structure data lakes and data warehouses? How does data governance affect the success of data science initiatives?
  3. 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.