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I am currently the Network Scientist with the Bank of America’s Enterprise Information Exchange group. Prior to that, I was the Chief Scientist at DeepMile Networks where I worked with a great team on various big data analysis projects, and a Research Associate in the Innovation and Entrepreneurship group at Imperial College Business School. This blog is mainly centered around my academic work on network science. Network science is the study of relational data (e.g., collaboration patterns, neural connections, or online communication). My work in this area started in 2003 when I founded a Facebook-like online community in California called ANTfriender. Although the venture failed, it proved to be a great source of network data. In fact, I wrote a Ph.D. on “The Structure and Evolution of Weighted Networks” using this data from the site. I have also authored a number of peer-reviewed articles on network analysis. Many of these papers stem from my interest in richer types of networks (e.g., weighted, two-mode, or time-stamped data) and my belief that the additional richness represents an enormous potential for exploring novel organising principles in networks. For more information on this line of work, see the publications-page and the Network Science-section, which highlights how to actually apply some of the proposed measures as well as related others in the literature.
Most of my research has been methodological in nature. The usefulness of methodological advances is lessened if they are not implemented in an easily accessible programme. To walk the walk, I have collected the functions to compute the ones I have proposed in an open-source software package called tnet. It is implemented in the free open-source statistical programme R to ensure that everyone can easily access it (even those using Macs or Linux).