Associate Editor: Nachiketa Sahoo

Boston University

Term: January 1, 2021 – December 31, 2022

Research Interest:

My research lies at the intersection of Machine Learning and Information Systems. Typically, it focuses on learning user preferences and decision making processes from large disaggregate user-activity datasets and applying these learnings to solve important problems facing businesses and society. I am particularly interested in the application area of personalized recommender systems and their implication for the businesses and consumers. I am also interested in studying the effects of various forms on online content on user engagement. My research has spanned domains of online news, social media, and online and offline retail.

Methodological interests:

My research often develops statistical models of users based on economic theories and estimates such models from user-activity datasets using approaches from statistical machine learning. I also use econometric modeling for causal inference from archival data.


I appreciate papers that,

  • make methodological contributions (algorithms/approaches) that carefully consider behavioral issues of human agents and that are rigorously validated through appropriate comparison to existing approaches or through theoretical analysis
  • carefully answer challenging causal inference questions with significant policy implications
  • have a strong theoretical component (brief thoughtful argument for the thesis building on relevant literature).

I am not well qualified to evaluate primarily qualitative research papers, although I do appreciate qualitative research components that provide insight into a studied domain.