Paramveer Dhillon recently completed his postdoctoral associate position at the MIT Sloan School of Management’s Initiative on the Digital Economy, and is now Assistant Professor at the University of Michigan.
Paramveer’s research broadly involves making careful empirical measurements (both causal and predictive) from big data and draws on ideas from Computer Science, Statistics, Sociology & Applied Econometrics. Substantively, he is interested in understanding the value drivers in digital & social products and the strategies that firms should adopt in developing them. In one stream of research he is investigating digital paywall strategy for newspapers (in partnership with The New York Times and Boston Globe) and in an another stream of research, he is working on finding efficient targeting strategies in social networks (to maximize the size of viral diffusion cascades).Paramveer holds an A.M. in Statistics and M.S.E & Ph.D. in Computer & Information Science (CIS), all from the University of Pennsylvania. His doctoral dissertation won the Morris & Dorothy Rubinoff Best Dissertation Award. His doctoral dissertation, 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 and has been published in top tier journals/conferences.
