Answer: just wait until you're completely unmotivated to do anything else. Sunny days with perfect weather are really the only times I get a chance to do any significant literature reviews. This afternoon, when I was unable to get myself to stay in my windowless office, I (finally) sifted through the WSDM proceedings that I'm most interested in, and read a couple papers on trust/distrust propagation. I'm getting better at adding papers to my bibsonomy [rss]. The top 10 or so should be what I covered today.
Also a fun article: via Physics Arxiv Blog, To How Many Politicians Should Government Be Left? The article looks at the "efficacy" of a government compared to its cabinet size, and makes a rather nifty model of how opinions are formed in small networks. Another interesting bit is that while cabinet size ranged from 5 to 54, not a single government of the nearly 200 surveyed had a cabinet of size 8-- apparently it is common knowledge that that is bad luck, or something.
I also discovered that Jure was smart enough to submit last year's SDM paper to ArXiv, which yielded a citation. That has prompted me to register so I can post other publications.
This is related to a recent pet peeve of mine-- the fact that it's difficult to get conference proceedings. The ACM/Citeseer folks don't always things from workshops and the like that I'm interested in. Most authors have the sense to post their papers on their websites, but I much prefer being able to get a conference all in one place. Of course, professional organizations don't like to do that. I find it hard to believe that they really make money off of conference proceedings, so I can only guess that it has to do with publisher/copyright/legalities rules outside their control. Maybe someday CC/GPL will be able to wrest away some control.
Thursday, April 17, 2008
Sunday, April 6, 2008
ICWSM, semi-supervised learning
Returned from ICWSM, and was inspired to perhaps start blogging again, but we'll see how long that lasts.
The tutorial at ICWSM went well (pdf slides available at that link, ppt available by emailing me). I will be giving it again at NESCAI. There were a lot of great talks and posters at ICWSM; a lot more toward the text/sentiment mining side of things than last year, but still a great variety of concepts.
While in Seattle I missed the 10-601 class lectures on semi-supervised learning, and had to prepare a recitation anyway. So as part of that preparation I came across a good survey paper by Xiaojin Zhu. It has an entire section devoted to graph-based methods, some of which I hadn't heard of, so this was useful to me beyond giving me interesting things to talk about in recitation. It might be of use to try some of these algorithms on community detection in networks.
The tutorial at ICWSM went well (pdf slides available at that link, ppt available by emailing me). I will be giving it again at NESCAI. There were a lot of great talks and posters at ICWSM; a lot more toward the text/sentiment mining side of things than last year, but still a great variety of concepts.
While in Seattle I missed the 10-601 class lectures on semi-supervised learning, and had to prepare a recitation anyway. So as part of that preparation I came across a good survey paper by Xiaojin Zhu. It has an entire section devoted to graph-based methods, some of which I hadn't heard of, so this was useful to me beyond giving me interesting things to talk about in recitation. It might be of use to try some of these algorithms on community detection in networks.
Tuesday, February 19, 2008
Open problems in movie stunt coordination
Via Fark, stuntman is attempting a 24-mile skydive.
But Steve, of East London, said: “It’s the last great challenge left on Earth. Obviously it will be dangerous. We’re playing with a lot of unknowns. But it’s my job to assess risk and I don’t believe the problems are insurmountable.”Last great challenge on Earth? Looks like all of us scientists can quit our jobs soon! :-)
Saturday, February 16, 2008
TREC blog retrieval
Jonathan Elsas presented in the Social Media Reading Group yesterday. He presented to us the very successful approach the CMU team took for the Blog Retrieval task at TREC 2007. Details are in this paper (pdf)
He brought up the point that TREC has the cool property of being "task oriented", which is not always the case with data mining research (and is a criticism of the 'what do evolving graphs look like?' approach I tend to take with my own research).
Another point he made is that no teams at TREC (successfully) used two common properties of data that *are* important in the non-task-oriented research in social media: timestamps and link analysis. He did not seem to think that simply aren't "useful" properties, only that nobody figured out how to use them properly.
While I think that link analysis could be used, it certainly could not be used without some significant text analysis. My impression is that link analysis is useful for tasks like measuring influence or information diffusion, or trust and authority. Relevance seems to me to be a much more text-dependent property.
Furthermore, relevance is a subjective measure, just like influence and authority. In fact, the difficulty in a lot of data mining research is the difficulty of finding a good evaluation of your results. TREC scored the entries with human-tagging. If the goal was to find relative blog posts, then each team's algorithm would find some candidate posts, and the competitors themselves would then vote on which seemed best. And that's probably the best we could do for something so imprecise as "relevance".
It's really hard to do "science" when such complex beings as humans are involved in the measurements.
Jon also kindly lent me the WSDM proceedings, which I copied to my laptop and intend to review soon.
He brought up the point that TREC has the cool property of being "task oriented", which is not always the case with data mining research (and is a criticism of the 'what do evolving graphs look like?' approach I tend to take with my own research).
Another point he made is that no teams at TREC (successfully) used two common properties of data that *are* important in the non-task-oriented research in social media: timestamps and link analysis. He did not seem to think that simply aren't "useful" properties, only that nobody figured out how to use them properly.
While I think that link analysis could be used, it certainly could not be used without some significant text analysis. My impression is that link analysis is useful for tasks like measuring influence or information diffusion, or trust and authority. Relevance seems to me to be a much more text-dependent property.
Furthermore, relevance is a subjective measure, just like influence and authority. In fact, the difficulty in a lot of data mining research is the difficulty of finding a good evaluation of your results. TREC scored the entries with human-tagging. If the goal was to find relative blog posts, then each team's algorithm would find some candidate posts, and the competitors themselves would then vote on which seemed best. And that's probably the best we could do for something so imprecise as "relevance".
It's really hard to do "science" when such complex beings as humans are involved in the measurements.
Jon also kindly lent me the WSDM proceedings, which I copied to my laptop and intend to review soon.
Tips on the Interview Process
Jeannette Wing, former CSD chair now at the NSF, came back to give her famous talk "Tips on the Job Interview Process". I was one of the "younger" grad students there-- since it had been three years since the last time she gave the talk there's the potential it wouldn't be given again in time for me to graduate. Also, sometimes the interview process for internships is similar.
Slides from the 2005 version of the talk.
Slides from the 2005 version of the talk.
Friday, February 15, 2008
Large-scale visualization reading group
Independent of the social media reading group (though I imagine some folks will participate in both), a visualization group has been founded by Peter Landwehr and Anita Sarma. And the first group meeting is on graph visualization (Thursday at 12:30 in the gradlounge). I'm stoked.
For the schedule and to subscribe to the mailing list, visit their wiki page.
For the schedule and to subscribe to the mailing list, visit their wiki page.
Sunday, February 10, 2008
Boycotting assistant professorships
I'm currently reading A Ph.D. Is Not Enough: A Guide To Survival In Science. There is one chapter devoted to deciding on a career path, mainly between academia and industry/government.
It brings up some good arguments against going into academia. One oft-cited reason is the "begging for money"-- while you have the freedom to study whatever you like, you're limited by what you can find funding for. One thing that isn't often mentioned is that even fully-tenured professors still don't get to do whatever they like. By the time someone has tenure they're pretty well-known and have a lot of administrative stuff to take care of. Giving invited talks and schmoozing with NSF officials tends to crowd out time to spend meeting with students, never mind actually doing nitty-gritty research like they did in the golden days of grad school.
According to the chapter, junior professors generally have that minus the job security. Grants are often awarded based on track-record, which junior profs haven't had time to get. Also, in the first few years they need to start teaching courses from scratch rather than re-using things from past years. And, of course, they need to make a place for themselves in the community by reviewing papers, serving on PCs, writing papers, mentoring students, etc. Then, if they don't get tenure, they have to go away and start all over somewhere else.
Feibelman makes an argument that we simply shouldn't stand for that. By accepting an asst. prof job as-is, one consents to that mistreatment by The Man. And the intense competition that goes on for these few prized positions isn't giving The Man any incentive to change the way he does things. Feibelman suggests that one closely evaluate his or her priorities and recognize the inherent bias one who's spent so long in school may have toward academia (as in, we want to emulate our heroic advisors). He also suggests the option of making a name for oneself in industry/government labs and then walking right up to a university and getting a tenured or almost-tenured job right away.
I would tend to agree that a 6+ year hazing period, if that is indeed what it is, isn't good for the system. It appears from the outside that of the set of {JuniorProfessorship, Sleep, Family}, a mortal being can pick two at most. And whining about it won't do jack until enough sought-after PhDs start making ultimatums. However, I am not convinced that the ones fighting for professorships are going into it blindly. Anybody who's been in grad school for a few months knows the demands on their professors. People who have a choice between academia and industry and choose academia are usually willing to make sacrifices somewhere, whether it means their family or their hobbies or their health. Scientific research is the greater good. People who actually score academic jobs probably have done little else besides work for the last 10 years of their life-- if they didn't like it that way they would have changed some time ago. It works for them.
On the other hand, being focused to the point of peripheral blindness does somewhat correlate with being in grad school, so maybe some people are shooting for academia without knowing all the costs.
It brings up some good arguments against going into academia. One oft-cited reason is the "begging for money"-- while you have the freedom to study whatever you like, you're limited by what you can find funding for. One thing that isn't often mentioned is that even fully-tenured professors still don't get to do whatever they like. By the time someone has tenure they're pretty well-known and have a lot of administrative stuff to take care of. Giving invited talks and schmoozing with NSF officials tends to crowd out time to spend meeting with students, never mind actually doing nitty-gritty research like they did in the golden days of grad school.
According to the chapter, junior professors generally have that minus the job security. Grants are often awarded based on track-record, which junior profs haven't had time to get. Also, in the first few years they need to start teaching courses from scratch rather than re-using things from past years. And, of course, they need to make a place for themselves in the community by reviewing papers, serving on PCs, writing papers, mentoring students, etc. Then, if they don't get tenure, they have to go away and start all over somewhere else.
Feibelman makes an argument that we simply shouldn't stand for that. By accepting an asst. prof job as-is, one consents to that mistreatment by The Man. And the intense competition that goes on for these few prized positions isn't giving The Man any incentive to change the way he does things. Feibelman suggests that one closely evaluate his or her priorities and recognize the inherent bias one who's spent so long in school may have toward academia (as in, we want to emulate our heroic advisors). He also suggests the option of making a name for oneself in industry/government labs and then walking right up to a university and getting a tenured or almost-tenured job right away.
I would tend to agree that a 6+ year hazing period, if that is indeed what it is, isn't good for the system. It appears from the outside that of the set of {JuniorProfessorship, Sleep, Family}, a mortal being can pick two at most. And whining about it won't do jack until enough sought-after PhDs start making ultimatums. However, I am not convinced that the ones fighting for professorships are going into it blindly. Anybody who's been in grad school for a few months knows the demands on their professors. People who have a choice between academia and industry and choose academia are usually willing to make sacrifices somewhere, whether it means their family or their hobbies or their health. Scientific research is the greater good. People who actually score academic jobs probably have done little else besides work for the last 10 years of their life-- if they didn't like it that way they would have changed some time ago. It works for them.
On the other hand, being focused to the point of peripheral blindness does somewhat correlate with being in grad school, so maybe some people are shooting for academia without knowing all the costs.
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