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 Dissertation Award given by Penn CIS Department.Contact Info:
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:
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.