Bio

Carlee Joe-Wong is a Ph.D. candidate and Jacobus Fellow at Princeton University’s Program in Applied and Computational Mathematics. Her research interests include network economics, distributed systems, and optimal control. She received her A.B. in mathematics in 2011 and her M.A. in applied mathematics in 2013, both from Princeton University. In 2013, she was the Director of Advanced Research at DataMi, a startup she co-founded in 2012 that commercializes new ways of charging for mobile data. DataMi was named a “startup to watch” by Forbes in 2014. Carlee received the INFORMS ISS Design Science Award in 2014 for her research on smart data pricing, and the Best Paper Award at IEEE INFOCOM 2012 for her work on the fairness of multi-resource allocations. In 2011, she received the National Defense Science and Engineering Graduate Fellowship (NDSEG).

Smart(er) Data Pricing

Smart(er) Data Pricing

Over the past decade, many more people have begun to use the Internet regularly, and the proliferation of mobile apps allows them to use the Internet for more and more tasks. As a result, data traffic is growing nearly exponentially. Yet network capacity is not expanding fast enough to handle this growth in traffic, creating a problem of network congestion. My research argues that the very diversity in usage that is driving growth in data traffic points to a viable solution for this fundamental capacity problem.

Smart data pricing reduces network congestion by looking at the users who drive demand for data. In particular, we ask what incentives will alter user demand so as to reduce congestion, and perhaps more importantly, what incentives should we offer users in practice? For instance, simply raising data prices or throttling data throughput rates will likely drive down demand, but also lead to vast user dissatisfaction. More sophisticated pricing schemes may not work in practice, as they require users to understand the prices offered and algorithms to predict user responses. We demonstrate the feasibility and benefits of a smart data pricing approach through end-to-end investigations of what prices to charge users, when to charge which prices, and how to price supplementary network technologies.

Creating viable pricing solutions requires not only mathematical models of users’ reactions to the prices offered, but also knowledge of systems-building and human-computer interaction. My work develops a feedback loop between optimizing the prices, offering them to users, and measuring users’ reactions to the prices so as to re-calibrate the prices over time. My current research expands on this pricing work by studying users’ incentives to contribute towards crowd-sourced data. Without properly designed incentive mechanisms, users might “free-ride” on others’ measurements or collect redundant measurements at a high cost to themselves.