Current Research

I am a fourth-year PhD student at Carnegie Mellon University and the Heinz College School of Information Systems and Management in Pittsburgh, PA, USA. My research agenda is on the societal impact of information technology, particularly in the urban context and on impacts beyond just a technology's primary sector.

This research is highlighted by three ongoing projects described below. My research methods range from machine learning methodology to economic theory to applied microeconomics. I incorporate this range of methods since they are useful to answer a range of related questions. Each project is motivated to provide insights into how changes in information technology may be affecting societal and secondary outcomes:

  • "Identifying Significant Predictive Bias in Classifiers" (the work is currently ongoing and joint with Daniel Neill)
    • The above arXiv link is a 5-page introduction to the project, presented, with a travel award, at the NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems, and later, the KDD 2017 Workshop on Fairness, Accountability, and Transparency in ML (FAT-ML).
    • This work is motivated by the increasing use of data-driven classifiers and risk assessment models for decision-making in various public and private sectors. Beyond overall performance assessment of these models, it's important that we identify if there are subpopulations or subgroups where such models may be over- or under-estimating the probability. With exponentially many such groups though, this can be a difficult problem. Our method enables such identification, providing both a way to audit the use of such models and improve them.

  • "Peer-to-Peer Transportation Platforms, Consumer Mobility, and Urban Consumption Patterns" (joint work with Beibei Li)
    • Initial work presented at WISE 2016 (Dublin) and accepted for oral presentation in Applied Data Science Track at KDD 2017 (9% acceptance rate).
    • This work is motivated by the popularity of peer-to-peer transportation platforms, like Uber and Lyft. We are interested in how the adoption of such services have affected the movement and local consumption of consumers. Particularly, we are interested in the heterogeneity of the effect, such as along spatial, demographic, or temporal dimensions. This is of interest to urban planners and policy makers, to better understand how the transportation system might influence local movement and consumption patterns. This work uses a unique longitudinal panel dataset on consumer transactions.

  • "Business Models in the Sharing Economy: Manufacturing durable goods in the presence of Peer-to-Peer rental markets" (joint work with Vibhanshu Abhishek and Jose Guajardo)
    • Submitted and under review Dec 2016. Presented at CIST 2016 (Nashville), WISE 2016 (Dublin), INFORMS 2016 (Nashville), POMS 2017 (Seattle, finalist for best student paper in supply chain mangement), The Marketing Science Conference 2017, and MSOM 2017.
    • This work is motivated by the rise of peer-to-peer rental markets for a variety of durable goods (e.g., vehicles, boats, bicycles, condominiums). We seek to provide a simple theoretical model from the perspective of manufacturers: (a) how are manufacturers affected by the entry of such peer-to-peer markets, and (b) what are the optimal business models in light of such markets? We model consumer usage as stochastic and find that consumer heterogeneity in such usage rates is the key interesting parameter that guides our results. When heterogeneity in usage rates is intermediate, we identify a new equalizing effect, a novel effect enabled by peer-to-peer markets that benefits the manufacturer. In such cases, a manufacturer may prefer to embrace peer-to-peer markets even over controlling and operating their own rental markets.