I am a computational social scientist and am interested in understanding the impact of digital technologies on our lives, and economy. My research broadly involves making careful empirical measurements (both causal and predictive) from big data, and draws on ideas from Social Sciences, Applied Econometrics, Computer Science, & Statistics.
Currently, in my main stream of research, I am investigating digital paywall strategy for newspapers. As part of other projects, I am working on using deep learning to identify unmet customer needs, and finding efficient targeting strategies in social networks (to maximize the size of viral diffusion cascades).
My research is supported by collaborations with The Boston Globe, The New York Times, and NerdWallet.
I hold an A.M. in Statistics and M.S.E & Ph.D. in Computer & Information Science (CIS), all from the University of Pennsylvania, where I was advised by Profs. Lyle Ungar and Dean Foster. My doctoral dissertation won the Morris & Dorothy Rubinoff Best Dissertation Award. My doctoral work, which was on statistical Machine Learning & Natural Language Processing (NLP), proposed simple linear spectral learning methods that give accuracies comparable to or better than state-of-the-art "deep-learning" algorithms on text data.