At a recent meeting of the Association for Computing Machinery, the main computer science association, the CEO of ACM John White reported on efforts to increase the visibility and understanding of computer science as a discipline. He asked “Where is the C in STEM?” (STEM stands for Science, Technology, Engineering, and Math, and there are many policy efforts to promote teaching and learning in these areas.) He argued that computer science is not just the “T” in “STEM”, as many might assume. Computer science deserves attention of its own from policy makers, teachers, and students.

I agree, but if computer science is not the “T”, then what is it? It’s funny. Computer science seems to span all the letters of STEM. It’s part science, part technology, part engineering, and part math. (Ironically, even though it’s called computer science, the “S” may be the least defensible.*)

The interdisciplinary nature of computer science can be seen throughout the university system: no one knows quite where CS departments belong. At some universities they are part of engineering schools, at others they belong to schools of arts and sciences, and at still others they have moved from one school to another. That’s not to mention the information schools and business schools with heavy computer science focus. At some universities, computer science is its own school with its own Dean. (This may be the best solution.)

Actually, I’d go one step further and say that computer science also involves a good deal of “A”, or art, as Paul Graham popularized in his wonderful book Hackers and Painters, and as seen most clearly in places like the MIT Media Lab and the NYU Interactive Telecommunications Program.

So where is the C in STEM? Everywhere. Plus A. Computer science = STEAM.**

__________
* It seems that those fields who feel compelled to append the word “science” to their names (social science, political science, library science) are not particularly scientific.
** Thanks to Lance Fortnow for contributing ideas for this post, including the acronym STEAM.

1. New York Computer Science and Economics Day (NYCE Day)

Monday, November 9, 2009 | 9:00 AM – 5:00 PM
The New York Academy of Sciences, New York, NY, USA

NYCE 2009 is the Second Annual New York Computer Science and Economics Day. The goal of the meeting is to bring together researchers in the larger New York metropolitan area with interests in Computer Science, Economics, Marketing and Business and a common focus in understanding and developing the economics of internet activity. Examples of topics of interest include theoretical, modeling, algorithmic and empirical work on advertising and marketing based on search, user-generated content, or social networks, and other means of monetizing the internet.

The workshop is soliciting rump session speakers until October 12. Rump session speakers will have 5 minutes to describe a problem and result, an experiment/system and results, or an open problem or a big challenge.

Invited Speakers

  • Larry Blume, Cornell University
  • Shahar Dobzinski, Cornell University
  • Michael Kearns, University of Pennsylvania
  • Jennifer Rexford, Princeton University

CFP: New York Computer Science and Economics Day (NYCE Day), Nov 9 2009

2. 11th ACM Conference on Electronic Commerce (EC’10)

June 7-11, 2010
Harvard University, Cambridge, MA, USA

Since 1999 the ACM Special Interest Group on Electronic Commerce (SIGecom) has sponsored the leading scientific conference on advances in theory, systems, and applications for electronic commerce. The Eleventh ACM Conference on Electronic Commerce (EC’10) will feature invited speakers, paper presentations, workshops, and tutorials covering all areas of electronic commerce. The natural focus of the conference is on computer science issues, but the conference is interdisciplinary in nature. The conference is soliciting full papers and workshop and tutorial proposals on all aspects of electronic commerce.

Quantifying New York’s 2009 June gloom using WeatherBill and Wolfram|Alpha

In the northeastern United States, scars are slowly healing from a miserably rainy June — torturous, according to the New York Times. Status updates bemoaned “where’s the sun?”, “worst storm ever!”, “worst June ever!”. Torrential downpours came and went with Florida-like speed, turning gloom into doom: “here comes global warming”.

But how extreme was the month, really? Was our widespread misery justified quantitatively, or were we caught in our own self-indulgent Chris Harrisonism, “the most dramatic rose ceremony EVER!”.

This graphic shows that, as of June 20th, New York City was on track for near-record rainfall in inches. But that graphic, while pretty, is pretty static, and most people I heard complained about the number of days, not the volume of rain.

I wondered if I could use online tools to determine whether the number of rainy days in June was truly historic. My first thought was to try Wolfram|Alpha, a great excuse to play with the new math engine.

Wolfram|Alpha queries for “rain New Jersey June 200Y” are detailed and fascinating, showing temps, rain, cloud cover, humidity, and more, complete with graphs (hint: click “More”). But they don’t seem to directly answer how many days it rained at least some amount. The answer is displayed graphically but not numerically (the percentage and days of rain listed appears to be hours of rain divided by 24). Also, I didn’t see how to query multiple years at a time. So, in order to test whether 2009 was a record year, I would have to submit a separate query for each year (or bypass the web interface and use Mathematica directly). Still, Wolfram|Alpha does confirm that it rained 3.8 times as many hours in 2009 as 2008, already one of the wetter months on record.

WeatherBill, an endlessly configurable weather insurance service, more directly provided what I was looking for on one page. I asked for a price quote for a contract paying me $100 for every day it rains at least 0.1 inches in Newark, NJ during June 2010. It instantly spat back a price: $694.17.



WeatherBill rainy day contract for June 2010 in Newark, NJ

It also reported how much the contract would have paid — the number of rainy days times $100 — every year from 1979 to 2008, on average $620 for 6.2 days. It said I could “expect” (meaning one standard deviation, or 68% confidence interval) between 3.9 and 8.5 days of rain in a typical year. (The difference between the average and the price is further confirmation that WeatherBill charges a 10% premium.)

Below is a plot of June rainy days in Newark, NJ from 1979 to 2009. (WeatherBill doesn’t yet report June 2009 data so I entered 12 as a conservative estimate based on info from Weather Underground.)


Number of rainy days in Newark, NJ from 1979-2009

Indeed, our gloominess was justified: it rained in Newark more days in June 2009 than any other June dating back to 1979.

Intriguingly, our doominess may have been justified too. You don’t have to be a chartist to see an upward trend in rainy days over the past decade.

WeatherBill seems to assume as a baseline that past years are independent unbiased estimates of future years — usually not a bad assumption when it comes to weather. Still, if you believe the trend of increasing rain is real, either due to global warming or something else, WeatherBill offers a temptingly good bet. At $694.17, the contract (paying $100 per rainy day) would have earned a profit in 7 of the last 7 years. The chance of that streak being a coincidence is less than 1%.

If anyone places this bet, let me know. I would love to, but as of now I’m roughly $10 million in net worth short of qualifying as a WeatherBill trader.

(First in a series of “random thoughts on science”)

A mind boggling number of academic research conferences and workshops take place every year. Each fills a thick proceedings with publications, some containing hundreds of papers. High-profile conferences can attract five times that many submissions, often of low average quality. Smaller venues can seem absurdly specialized (unless it happens to be your specialty). Every year, new venues emerge. Once established, rarely do they “retire” (there is still an ACM Special Interest Group on the Ada programming language, in addition to a SIG on programming languages). It’s impossible for all or even most of the papers published in a given year to be impactful. Most of them, including plenty of my own, will never be cited or even read by more than the authors and reviewers.

No one can deny that incredible breakthroughs emerge from the scientific process — from Einstein to Shannon to Turing to von Neumann — but scientific output seems to have a (very) long tail.

Is this a good thing, a bad thing, or just a thing?

Is the tail…

Good?
Is the tail actually crucial to the scientific process? Are some breakthroughs the result of ideas that percolate through long chains — person to person, paper to paper — from the bottom up? Is science less dwarfs standing on the shoulders of giants than giants standing on the shoulders of dwarfs? I published a fairly straightforward paper that applies results in social choice theory to collaborative filtering. Then a smarter scientist wrote a better paper on a more widely applicable subject, apparently partially inspired by our approach. Could such virtuous chains actually lead, eventually, to the truly revolutionary discoveries? Is the tail wagging the dog?
Bad?
Are the papers in the tail a waste of time, energy, and taxpayer dollars? Do they have virtually no impact, at least compared to their cost? Should we try hard to find objective measures that identify good science and good scientists and target our funding to them, starving out the rest?
Ugly?
Is the tail simply a messy but necessary byproduct (I can’t resist: a “messessity”) of the scientific process? Under this scenario, breakthroughs are fundamentally rare and unpredictable hits among an enormous sea of misses. To get more and better breakthroughs, we need more people trying and mostly failing — more monkeys at typewriters trying to bang out Shakespeare. Every social system, indeed almost every natural system, has a long tail. Maybe it’s simply unavoidable, even if it isn’t pretty. Was the dog simply born with its (long and scraggly) tail attached?

Translate this Page

© 2010 Oddhead Blog Suffusion WordPress theme by Sayontan Sinha