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?
These slides are from a presentation I gave in Sep Kamvar’s Computational Methods in Data Mining (old website link here). In the presentation, I presented TurKit, a programming framework created by Greg Little and others at MIT that allows for programmatic iteration over tasks in Mechanical Turk. Essentially, that means that instead of the familiar paradigm of sending out a bunch of HITs and waiting for the responses, TurKit will ping AMT for answers and these answers can be used in future HITs. This allows for the use of an “improve and vote” loop, where Turkers continually improve on and validate the work of other Turkers. They had some impressive results in the paper, getting fairly high quality responses to a wide range of tasks (including image labeling, handwriting recognition, and brainstorming) for under $0.50.
The buzz around Q&A startup Quora has been building steadily over the past couple of months. I measure this not only by the number of Follow messages received concerning people randomly sampled from my Facebook connections which are now flooding my inbox, but also by the heated debate that is developing about the site’s usefulness, much of which is chronicled in this TechCrunch article about the “Quora Backlash Backlash”.
This quarter, I’ve been taking two classes: Data Visualization, taught by Jeff Heer (my rotation advisor for this quarter), and Social and Information Network Analysis, taught by Jure Leskovec (my rotation advisor for next quarter). If you’re interested in either of these two topics, follow those links, as an extensive set of course materials (including class projects and suggested readings) have been posted. For a mid-quarter assignment, I worked with Diana MacLean on a project related to visualizing social network patterns. In this project, we chose to examine methods for visualizing cross-posting behaviors of users of MedHelp, an Online Health Community (OHC).
I’m embedding a presentation I gave at a recent “Data Lunch” about how to analyze responses to Likert items. As I am not a stats expert in any respect, I learned a number of things while putting this together – one of the most important is that Likert isn’t actually pronounced “Like-ert”, it’s pronounced “Lick-ert”, which is still tough for me to remember to say. Anyways, hope you enjoy, I’ll include some summary below as well.
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):
I’ve been interested for a while now in how information and behavior can spread through social networks; an important sub-topic in this field is the spread of health behaviors. This area of study is especially important in understanding the behaviors of adolescents, as there are a number of unhealthy behaviors (ranging from drug use to unhealthy eating to unsafe sex practices) which start in adolescence, persist into adulthood, and contribute to some of the leading causes of death and disability.
As any parent or educator will likely tell you, the behavior of teens closely linked in a social network will often display many similarities: teens who smoke or drink, for instance, are often friends with other teens who smoke or drink. By establishing and tracking the spread of these behaviors scientifically, we can gain a greater understanding of the mechanisms at work and perhaps harness them to help spread healthy behaviors instead of unhealthy ones.
Yesterday, I attended a pretty interesting PARC Forum where the speakers were three members of the Wikimedia Foundation. For those, that don’t know, Wikipedia is actually part of a larger group of projects (including Wiktionary, Wikiquotes, Wikiversity, etc.) which are all under the umbrella of the Wikimedia foundation, but the talks primarily focused on Wikipedia and how the foundation leverages the community of editors and developers to help build the content and tools that make the site work. PARC will have the video up in a couple days if you want to watch, and you can find the presentation here, but I’m presenting a short summary of some of the interesting tidbits and points here, organized by speaker:
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.
