I am starting my fourth year as a PhD student in the Societal Computing program at the Institute for Software Research (ISR) at Carnegie Mellon University, where I am advised by Prof. Jim Herbsleb.

My research involves studying problems of coordination and collaboration in online communities with a specific focus on open-source software development. There are two things that intrigue me about open-source in general – the highly distributed and fragmented nature of development, and the fact that explaining their behavior can often be as complex as human behavior. Like most folks at CMU, I would like to call my research inter-disciplinary, cutting across software engineering, social science, and organizational behavior (and may be bit of electrical engineering, psychology, and chemistry). Anyway, I think there is hardly any research these days that is non-interdisciplinary.

Research without data is like life without coffee! My research problems require data primarily from sources like Github, Wikipedia and mailing lists. A lot of what I do with data can be called "predictive modeling", using established statistical techniques. Although I plan on using advanced machine learning and deep learning techniques in the near future. I am, however, a firm believer that the true meaning (or patterns) in data can only emerge with a thorough and rigorous application of qualitative analysis. So, most of my studies are mixed-methods, where often in my case the first part involves generating hypotheses using qualitative analysis techniques and then testing them quantitatively.

Prior to my foray into the "enlightening" world of academia, I lived in the southern part of India, worked for companies like Novell, Verizon and Hewlett Packard, wrote code and ate rice.

"If you want to wake up in your bed and believe whatever you want to believe, then click here. But if you want to stay in wonderland and know how deep a phd can take you, then click here." ...courtesy: The Matrix