Paramveer Dhillon

Starting August 2014 I am a Postdoctoral Associate at the MIT Sloan School of Management working with Prof. Sinan Aral. I hold an A.M. in Statistics and M.S.E & Ph.D. in Computer & Information Science (CIS), all from the University of Pennsylvania.

During my Ph.D I was fortunate to have been advised by Profs. Lyle Ungar, Jim Gee and Dean Foster. My Ph.D thesis won the 2015 Morris and Dorothy Rubinoff Best Dissertation Award.

Contact Info:
77 Massachusetts Avenue
Cambridge, MA, U.S.A

My current research interests lie in Causal Inference for Network Science, Digital Experimentation, Quantitative Marketing & Statistical Machine Learning.

The focus of my research has always been to build statistical models which are driven by data and at the same time powered by strong theory.

During my Ph.D I worked on developing Spectral Learning algorithms for Natural Language Processing (NLP) and Brain Imaging. My thesis showed that simple linear models give accuracies comparable to or better than state-of-the-art "deep-learning" algorithms on data from two diverse domains-- Text/NLP and Brain Imaging. In addition, the Spectral Learning methods have strong theoretical grounding, which is absent for deep-learning based approaches.

My other grad-school research provides theoretical and empirical macro-level contributions like:

  1. Showing that Principal Component Regression (PCR) can never be much worse than Ridge Regression, but can be infinitely better. (Citation: JMLR 13)
  2. Extending Spectral Learning algorithms to trees, particularly the ones arising from dependency structures in natural language. (Citation: EMNLP 12)
  3. Formulating optimal information theoretic coding schemes for feature/covariate selection. (Citations: JMLR 11, ICDM 08, ECML 09, ACL 09)
  4. Information Extraction from structured Web data in the presence of a). Domain-specific constraints on the records to be extracted and b). Scarce availability of labeled training data. (Citations: CIKM 11, AISTATS 12)

I spent the summer of 2011 interning with the Machine Learning group at Yahoo! Research, Santa Clara, CA. In past, I have also interned at Max Planck Institute for Biological Cybernetics, Tübingen, Germany and Information Sciences Institute, USC, Marina Del Rey, CA.

For more details you might want to have a look at my Publications (Google Scholar Profile) or CV.