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Back when I was just starting graduate school, I remember already feeling as if I understood the components needed for great scientific research: knowledge of a domain, the ability to implement a system or execute an experiment, and a creative insight about a phenomenon worth studying. While the domain knowledge and ability to execute seemed like pre-requisites for doing science at all, the capacity for creativity seemed to the element that separated a great scientist from the good. Since I felt like I was good at identifying creative research, I hoped that once I immersed myself in academia and started gaining domain knowledge and engineering skill, the creative ideas would come to me. Now, almost a year into my PhD program, I feel like I have learned a great deal, but I am left with the question: Where are all those good ideas?
I’m currently reading Albert-László Barabási’s second book, Bursts. Though the book is primarily about predicting human behavior in the future, the book is peppered with interesting anecdotes about historical figures (i.e. from the past). One such figure mentioned prominently is Siméon-Denis Poisson, the 19th-century French mathematician. A element which may seem trivial out of context but is rather crucial in the book is Barabási’s description of Poisson’s organizational habits (a sort of 19th-century French GTD):
For a comprehensive look at applying for NSF (and other similar fellowships), you should check out Philip Guo’s Fellowships Tips page, which is really good and very comprehensive. I personally learn best by example, so in this post, I’d like to provide a personal perspective on the application and review process; hopefully, this will prove helpful to some of you applying in the coming fall. While I did not win this year, I think it’s helpful to see the essays of others with reviews to get a real sense of what the reviewers are looking for.
Earlier this week, the team at Aardvark unveiled a new paper “The Anatomy of a Large-Scale Social Search Engine” which will be presented in April at WWW 2010. Inspired by and patterned after “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, which describes the PageRank algorithm which drives Google’s search ranking system (which as Aardvark’s blog points out, was also presented at WWW 12 years ago). The paper, by Aardvark’s Damon Horowitz and Stanford’s Sep Kamvar, focuses mostly on the architecture of the Aardvark system, from the external representations with which users interact to the internal ranking algorithms on which the system runs. Below, I present a short summary of what they report, focusing on the elements I found most interesting.
Last month, I read a study by B. J. Fogg and others from the Persuasive Technology Lab at Stanford, entitled “What Makes Web Sites Credible? A Report on a Large Quantitative Study”. The paper described an early effort to systematically determine how different elements of web sites affect people’s perceptions of credibility (defined roughly as the intersection of trustworthiness and expertise). The original study design had 1400 participants completing a survey which presented them with 51 web site elements and asked them to rate how much more or less each element would affect the believability of a web site. The two questions I hope to answer, roughly, are “What has changed in the past 10 years about how people assess web site credibility?” and “Is there a cheaper, yet effective, way to do a study like this?”. The results have implications for website design.
