Introduce Concepts in Historical Order?

Let me confess: I have read very few scientific classics in the original.

I haven't read the Principia, the Origin of Species, or the Elements.

I had not even read Einstein's 1905 classic on Brownian motion, until a few years ago, even though half of my research is directly or indirectly animated by it.

Ever since I saw this amazing series on complex numbers, I have been wondering whether presenting the historical progression of ideas might be "better" than the standard textbook introduction. Here are some of my observations.

The historical approach (HA) is inherently interesting, because it is about ideas and the people behind them. Stories of humans exploring and pushing boundaries, regardless of domain, are fascinating. These stories often have imperfect people grappling with new ideas, getting confused by their implications, arguing back and forth, improving, and gradually perfecting them over centuries. This happened with classical mechanics, evolution, complex numbers, quantum mechanics, etc.

The standard approach (SA), on the other hand, steers away from messy pasts, leaps of intuition that came seemingly from nowhere, the entertaining bickering, and the trials and errors. It trims away the excess fat of distractions, consolidates different viewpoints, and presents a sanitized account of an idea. It is, without question, the quickest and cleanest way to learn a new concept. This is an extremely desirable feature in university courses, which have a mandate to "cover" a set of concepts, often in limited time.

Perhaps, a good practical compromise is to start with an example rooted in the historical approach to motivate the topic,  and transition to the standard textbook approach to teach the meat of the topic. It might be interesting to conclude once again with a historical perspective, perhaps mixed with a discussion of the current state of art and open questions.

Education, Second Chances, and Transformations

For some random reason today, I was reminded of a story that happened when I just started my current academic position.

I was a very eager assistant professor teaching thermodynamics to undergraduates. I had not yet been bathed in the richness of personal struggles that many students dragged with them into the classroom.

So after my first class, this kid - let's call him MJ - walked into my office. He delivered what seemed like a prepared two-minute talk on how he was going to focus, work hard, and turn things around that semester.

After he left, I looked up his past grades, and found that he was struggling with a GPA of less than 2.5, and an academic history that was littered with Cs and Ds. 

I did not think much about the incident, until the semester had picked up some steam. MJ showed up during office hours regularly. Although he was rusty in multiple areas, his dogged effort was palpable.

Over time, I got to know a little more about the social structure in which he had been surrounded - an ugly milieu that was riddled with gangs, drugs, and far worse - apathy.

What MJ lacked in mathematical ability, he tried to make up with logic, unconventional thinking, and tenacity.

He had that "born-again" zeal you can sometimes see in people who are given second chances, who have determined that this is it. It is now or never!

Thanks to the compounding effect of knowledge, he started making rapid progress. Within a month, I realized that he really had a good shot at turning this thing around, and actually found myself rooting for him. I really wanted it to end well for him.

He finished the course with a well-deserved A. I found out later that he had gotten As in most of his other classes that semester.

I watched him land a well-paying job a year later.

I saw him once more after that, when he came to recruit for "his" company, and caught up with him. He was doing well, and he had dramatically changed his family's trajectory.

The lesson: saving or helping one individual might not make a big difference to the world, but it does make a world of difference to that one individual.

McGowan on the PhD glut

John McGowan presents a well-sourced article (and somewhat old article) on the status of surplus PhDs we've been producing over the last thirty or so years. The appendices link to a number of interesting articles and reports on the topic.

By and large, I agree with the central thesis that the current academic model encourages the production of ever greater numbers of PhD students.

It is perhaps too much of a good thing.

Universities are driven increase the supply of PhDs. Students, on the other hand, are driven by the promise of more lucrative or meaningful employment. From a simplistic economic viewpoint, the demand for PhD students from universities exceeds the demand for doctorates. Thus, the incentives of universities are not necessarily aligned with the incentives of students, as I also remarked a while ago.

Having said that, and essentially murmured my agreement with the author, I think two of the arguments he lays down require additional qualification.

It is worth considering some basic arithmetic. A typical professor has at least two graduate students at any time, sometimes many more. A Ph.D. programs in the United States usually lasts 5-7 years. A professor will take on and “advise” students from about age 30 as a starting assistant professor to retirement or death (say 65). This means a typical professor produces at least ten Ph.D.’s during his or her academic career in his or her academic specialty. If all the students pursue an academic career, certainly the ambition of many, this means an additional nine (9) new positions must be created over 35 years on top of the professor’s own position. Even with a fifty percent drop out rate, at least four or five new positions must be created to absorb the new Ph.D.’s. Of course, nothing of the kind has been the case for over forty years.

From my anecdotal experience (chemical engineering in the  US) the fraction of PhD students seeking an academic position is much smaller than 50%. I would guess that it is closer to 10-20%. I am sure the numbers are different for theoretical physics, organic chemistry, petroleum engineering, etc., although I recognize that even these small levels would cause a "glut".

For many students, academia is not the endgame they seek.

There are more lucrative positions in the industry, that often offer starting salaries that are about 50% greater than in academia. I am sure a large fraction of the PhD quants on Wall Street, did not end up there because they could not find academic jobs. The menu of possible jobs for PhDs has certainly diversified in the past couple of decades.

The second is the argument about the unsustainability of the PhD production growth rate outstripping the GDP. Superficially, the argument makes sense. But the GDP is an aggregate of industries in decline (manufacturing) and in explosive growth phases (IT). It is quite possible that fraction of GDP due to PhD-level high-tech jobs is mounting a secular advance. I don't know if this is true (perhaps it is not), but unless that is ruled out, the "conclusion" is really a hypothesis to test.

Universities v/s MOOCs

Aswath Damodaran has two excellent posts on the unsuccessful onslaught of MOOCs on traditional universities, and the lessons that universities should learn, before the next wave of more potent attacks is unleashed.

[Business as usual would be a mistake], analogous to music companies reacting to the demise of Napster more than a decade ago by going back to their old modes of business (selling CDs through music stores), only to be swept away by Apple iTunes a few years later. The MOOC model represented the first serious foray of online entities into education and like Napster, it failed because it not only came with flaws but because it's promoters failed to fully understand the business it was trying to disrupt.

Universities are not merely vehicles to deliver content; instead they offer a bundled product which includes screening, certification, networking, entertainment, friendship etc. Each university offers a different, or differently weighted mix of "services" in the bundled product. Choosing which university to go to involves choosing the bundled product that best matches individual preferences and budgets.

If you are a faculty member or a college administrator, ... you have to look at what it is that you offer (as a college or university) that makes your education bundle unique, different and difficult to replicate (either online or in another institution). If you are an online education entrepreneur, your task is to figure out ways to unbundle the product and probe its weakest points.

In the second of the two posts, he elaborates on the latter. His summary is really spot on (emphasis mine).

I believe that change is coming to education but that it will come in stages and be under-the-surface. The first to feel the heat (if they have not already) will be colleges that have loose or non-existent screens, mechanized degree programs, content-heavy but learning-light classes and nonexistent networks. As they fall prey to online or alternative education systems, it is an open question as to how schools further up the food chain will react. I won’t claim to know the mindset of faculty/administrators at the top schools but my interactions with them suggest that many of them will, for the most part, resist change (especially if it inconveniences them) and argue that there is no chance that their civilized citadels will fall to the barbarians. But they are fooling themselves, since the disruptors have the luxury of being able to experiment, with nothing to lose, until they find the weapons that work. It is only a matter of time!

World's got Talent: Devlin on MOOCs

I read a provocative piece called "The Darwinization of Higher Education" by Keith Devlin, in which he makes a persuasive case for the talent sniffing abilities of MOOCs (massively open online courses).

He contends that the putative goal of such courses (mass education) has less to do with their real benefits:

Forget all those MOOC images of streaming videos of canned lectures, coupled with multiple-choice quizzes. Those are just part of the technology platform. In of themselves, they are not revolutionizing higher education. We have, after all, had distance education in one form or another for over half a century, and online education since the Internet began in earnest over twenty-five years ago. But that familiar landscape corresponds only to the last two letters in MOOC ("online course"). The source of the tsunami lies in those first two letters, which stand for "massively open."

Rather than focus on 90% of the students who drop out, identifying the few dozen who survive and flourish gives one an easy way to scour for talent. Quoting (emphasis mine)

At the level of the individual student, MOOCs are, quite frankly, not that great, and not at all as good as a traditional university education. This is reflected (in part) in those huge dropout rates and the low level of performance of the majority that stick it out. But in every MOOC, a relatively small percentage of students manage to make the course work to their advantage, and do well. And when that initial letter M refers not to tens of thousands but to "millions," those successes become a lot of talented individuals.  

One crucial talent in particular that successful MOOC students possess is being highly self-motivated and persistent. Right now, innate talent, self-motivation, and persistence are not enough to guarantee an individual success, if she or he does not live in the right part of the word or have access to the right resources. But with MOOCs, anyone with access to a broadband connection gets an entry ticket. The playing field may still not be level, but it's suddenly a whole lot more level than before. Level enough, in fact.

I've already seen numerous anecdotal variants of this model of talent identification work in the realm of open-source software. In fact, the employer of one of my grad-school room-mates found him via his presence and contribution to the Linux ecosystem.

Kling on Online Education

I recently listened to a podcast of an engaging interview with Arnold Kling at EconTalk. The interview starts off from an article Kling wrote earlier in the American. He starts off the article provocatively:

Education is in some respects one of the most stagnant of all major industries. A farmer from 150 years ago would not comprehend a modern farm. A factory worker from 150 years ago would not be able to function in a modern factory. But a professor from 150 years ago could walk into a classroom today and go to work without missing a beat.

At this point you are probably thinking, "this guy sounds like yet another of those guys who thinks online education and the private sector are going to supplant traditional universities." Perhaps, but not quite.

In the article, Kling then argues why MOOCs (massive open online courses) are mostly just hype. Most (about 90%) of the "tens of thousands" of students who take them, give up very early.

We should not be surprised that MOOCs do not benefit most of those who try them. Students differ in their cognitive abilities and learning styles. Even within a relatively homogenous school, you will see students put into separate tracks. If we do not teach the same course to students in a single high school, why would we expect one teaching style to fit all in an unsorted population of tens of thousands? 

An online course that has been designed at Stanford is likely to best fit the students who are suited to that particular university. The other beneficiaries are likely to be students who have the right cognitive skills and learning style but happen to be unable to attend college in the United States.

And perhaps a key insight:

The attempt to achieve large scale in college courses is misguided. Instead of trying to come up with a way to extend the same course to tens of thousands of students, educators should be asking the opposite question: How would I teach if I only had one student? Educators with just one student in their class would not teach by lecturing.

 Interesting perspective - even if you don't agree with all of it.

Efficiency and Friction in Academia

After reading Barry Schwartz's opinion column in the NYT, I wondered if his "efficiency-friction" metaphor had any resonance in an academic setting. To paraphrase poorly, Schwartz thinks efficiency is generally a good thing, while friction sometimes provides a useful counter-balance.

Almost instantly after I had framed that question, I recalled how hard it was to carry out some routine research tasks when I first "started", less than 15 years ago.

As an undergraduate student doing some literature survey, I remember the hurdles one had to jump over - first, one needed to check out a Chemical Abstracts or Inspec CD, and try to hit the static databases with meaningful queries. After studying the abstracts, one hoped to come up with some leads. I shudder to imagine what people had to do before CDs became cool.

Then, one had to figure out where the particular journal was archived (if it was at all) to hunt down the article.

After spending some time reading the article, one had to figure out if it was worthwhile to "save" it. If it was, then one headed with the big tome (usually several) to the photocopying unit, usually on a different level in the library.

The process of coming back from the library with a couple of useful articles was a full afternoon's workout.

Contrast those times with today.

I sit in my office, do a quick google search, and a couple of mouse clicks later, I have downloaded a PDF on my computer.

In rare cases, the article is very old, or the journal is not housed in our library.

No problem! I simply fill out simple form on my library's webpage, and usually I get a PDF emailed to me in a couple of days.

I can open up the PDF, read it, annotate it, and upload the annotated copy on a site like CiteULike, with some useful tags. I don't even have to store a copy on my Desktop. I can search (re-"search") for it whenever I like, I can read it from anywhere, and I can easily cite the paper.

If "Efficiency" ever wrote an autobiography, it would be littered with such anecdotes.

So no, I don't see any role for friction in literature surveys.

The only places that I can think of where efficiency sometimes becomes too much of a good thing  are classes with an over-reliance on Powerpoint (link to comics) lectures.

It is no wonder that some of the most popular material on the web (including Khan Academy, or lectures on MIT's OCW) tends to be very "old-school" chalkboard talks.

Hard Work versus Intelligence

I am currently reading Jonah Lehrer's fascinating book "How We Decide". If the rest of the book lives up to the three chapters I have read so far, I will recommend it enthusiastically.

In chapter 2, there is an intriguing discussion of the role of mistakes in the learning process, that are potentially relevant in an academic setting (apparently, Neils Bohr once remarked that an expert was a person who had committed all the mistakes possible in a narrow field).

The whole idea is that mistakes aren't things to be discouraged, but rather they should be "cultivated, and carefully investigated". Lehrer talks about a series of experiments performed by Stanford psychologist Carol Dweck on the correlation between future performance and quality of praise.

A bunch of fifth-graders  students were given a relatively easy test. Half the kids were praised for their intelligence ("you must be smart at this"). The other half were praised for their effort ("you must have worked really hard").

These kids were allowed to choose the level of difficulty of their next exam. The first choice was described as hard, but educational, while the other was described as being similar to the one they had just taken.  

90% of the kids praised for effort chose the harder test, while a majority of the kids praised for intelligence picked the easy test. They shunned the risk of making mistakes. This aversion can seriously inhibit learning.

Dweck then gave all the kids yet another test. This one was really really hard. In fact, it was written for eighth-graders. Dweck wanted to see how kids respond to the challenge. The "effort" kids got very involved, and tried to tease the test apart, while the "intelligent" group got easily discouraged.

After the test she asked the two groups of students to make a further choice. They could look at the exams of kids who did better than them, or worse than them. The "intelligence" kids almost always chose to bolster their self-esteem by comparing themselves with someone who had done worse. The other cohort were more interested in the higher-scoring exams - "trying to understand their mistakes, to learn from their errors , to figure out how to do better."

She was not done yet.

She gave one final "exit" exam, which was supposed to be similar to the initial test. The "intelligent" group saw their scores drop by 20% on average, while the other group improved by 30%.

As a teacher and parent, this is really important practical stuff. In fact here (pdf) is a relatively recent article on how to raise smart kids by Dweck.

Elite Institutions and Career Earnings

I finished reading Charles Wheelan's interesting book called "Naked Economics: Undressing the dismal science". It is a highly entertaining book for anyone with even a passing interest in how the world around us works. In tone, it resembles Freakanomics, but in terms of span, it feels much wider and more comprehensive, perhaps because it is not merely a collection of vignettes.

In one of the chapters, he points out to an interesting study by Krueger and Dale (2002). Graduates of highly selective schools earn higher salaries later in life than graduates of less selective schools. This does not seem very surprising.

Next, they examined the outcomes of students who were admitted to both a highly selective school, and a moderately selective school. The outcome (also the title of their 2002 paper) was that "Children Smart Enough to Get into Elite Schools may not need to Bother."

There is a more recent follow-up to that study, essentially reiterates the same conclusion.

The average SAT score of the most selective school a student applies to, is the best predictor of his or her future (monetary) success.

There is an important caveat. Minorities and other disadvantaged students gain the most from choosing an elite school over a less selective one.

Here's an interesting summary of what that means in practical terms:

Mr. Krueger gets the last word: 

My advice to students: Don’t believe that the only school worth attending is one that would not admit you. That you go to college is more important than where you go. Find a school whose academic strengths match your interests and that devotes resources to instruction in those fields. Recognize that your own motivation, ambition and talents will determine your success more than the college name on your diploma.

My advice to elite colleges: Recognize that the most disadvantaged students benefit most from your instruction. Set financial aid and admission policies accordingly.

Achievement Gap

In this nearly hour-long video (via Bridging Differences), an assortment of panelists discuss the nature of the achievement gap between races. All of them agree that there are no simple solutions, which in itself is interesting since both Diane Ravitch and Michelle Rhee are on the panel.

At one point, Ravitch reasserts her view that the original idea behind standardized testing was purely diagnostic. It was meant to be used like a thermometer is used to check temperature. Its widespread current use in penalizing or rewarding schools and teachers defeats that original intent. Comer backs her up by saying that a  thermometer can tell whether a patient has a fever, but tells us nothing useful about what caused it, or how to fix it.

In response, Rhee contends that once you find something amiss in the diagnosis, you do something about it, right? The idea that measurement and a corrective response to that measurement are completely independent of each other is misguided. If students under a particular teacher get low scores year after year, then at some point, one has to consider the hypothesis that the teacher needs to go.

Angel Harris also points out that anecdote is not data. Just because a certain model has worked once somewhere doesn't prove that it is a successful model. You have to consider the entire distribution of outcomes under that model.

A very interesting civil conversation.

Grade Inflation Links

1. A nice Infographic: A student from my department (Ian) pointed out that while the graphic is nice, the "embed me code" seems to tailored to improve its Google PageRank. At some point they may start advertizing links to online colleges and collect a fair amount of money!

2. A treasure trove of data and analysis (check out some of the links) on grade inflation.

Is higher education a bad value proposition?

Two recent "finance/econ" type articles seem to take opposite sides in this debate. On the one hand Vikram Manasharmani argues that higher education exhibits all the tell-tale signs of a classic bubble (it is useful to keep the recent US housing crisis looming in the background).

These include, among others (a) an unquestioning faith in the "assets" value, (b)  availability of easy credit to buy the asset, and (c) increasing participation of value-insensitive buyers.

The net result has been that the price of higher education has outstripped inflation in recent years by more than 5% at public institutions. The total student loan debt is apparently on track to beat the total credit card debt this year.

The other side of the debate comes from unemployment statistics. Saj Karsan presents a chart which breaks down unemployment numbers by education level.

It is nearly 15% for people without a high-school diploma, and about 4.4% for people with bachelors degree. As he notes:

Note that the overall unemployment rate in 2007, when the American economy was booming, was 4.6%, which is higher than the current unemployment rate of 4.4% for those with Bachelor's degrees. This data suggests (although it does not prove) that there is a shortage of educated workers in the US.

Why we have college.

Louis Menand in the New Yorker examines the issue.

Soon after I started teaching there (a public school), someone raised his hand and asked, about a text I had assigned, “Why did we have to buy this book?

I got the question in that form only once, but I heard it a number of times in the unmonetized form of “Why did we have to read this book?” I could see that this was not only a perfectly legitimate question; it was a very interesting question. The students were asking me to justify the return on investment in a college education. I just had never been called upon to think about this before. It wasn’t part of my training. We took the value of the business we were in for granted.

Is the role of higher education to sort students according to intelligence, skill or merit, or is it to ensure that everyone has access to knowledge and the goodies that accompany it? As he argues:

A lot of confusion is caused by the fact that since 1945 American higher education has been committed to both theories. The system is designed to be both meritocratic (Theory 1) and democratic (Theory 2). Professional schools and employers depend on colleges to sort out each cohort as it passes into the workforce, and elected officials talk about the importance of college for everyone. We want higher education to be available to all Americans, but we also want people to deserve the grades they receive.

And one of the many facts that I did not know

In 1940, the acceptance rate at Harvard was eighty-five per cent. By 1970, it was twenty per cent. Last year, thirty-five thousand students applied to Harvard, and the acceptance rate was six per cent. ... Columbia, Yale, and Stanford admitted less than eight per cent of their applicants. This degree of selectivity is radical. To put it in some perspective: the acceptance rate at Cambridge is twenty-one per cent, and at Oxford eighteen per cent.

It is an interesting read.

Gilbert Strang: Video Lectures

Gilbert Strang is an amazing teacher.

This summer, I've been watching some of his lecture videos on Linear Algebra, and Computational Science and Engineering, at MIT OpenCourseWare.

Although, they are not technologically fancy or gimmicky, they provide a superb introduction.

Here are short-cut links to:

1. Linear Algebra: I was still an undergrad when these lectures were filmed. As a matter of fact, I was taking a linear algebra course, at around the same time.
 
2. Computational Science and Engineering: Given my current academic home, this is a nice introductory series.

Scott Adams on Real Education

Interesting perspective from the creator of Dilbert. He begins with:

I understand why the top students in America study physics, chemistry, calculus and classic literature. The kids in this brainy group are the future professors, scientists, thinkers and engineers who will propel civilization forward. But why do we make B students sit through these same classes? That's like trying to train your cat to do your taxes—a waste of time and money. Wouldn't it make more sense to teach B students something useful, like entrepreneurship?

  And ends with:

Remember, children are our future, and the majority of them are B students. If that doesn't scare you, it probably should. 

In many ways, I think it is a critique of standardized testing (which automatically fosters standardized education).

What's your Nobel number?

We like quantifying things. We design impact factors, and h-indices to determine the scientific worth of a journal, or a researcher. Sometimes these numbers are meaningful. Sometimes they are gamed. Sometimes they are misused.

My pet peeve against these measures (and their extended family) is that they are designed to measure popularity. Unfortunately, they are commonly used as a proxy for scientific quality.

Can high-quality stuff be popular? You bet.

But the relationship between quality and popularity is tenuous at best.

High-quality stuff can stay under the radar for prolonged periods, and flashy low-quality stuff can go platinum. We intuitively understand and appreciate this difference when we judge music, movies, politics, or literature.

Love them or hate them, we probably have to learn to live with them.


There is another class of numbers, like the Erdos number, that exist for pure entertainment and tongue-in-cheek bragging value. They measure proximity to greatness, and are related to the six-degrees of separation idea.

Let us define a new number (maybe it already exists) which we shall call the "Nobel number" which measures the shortest "collaborative distance" between a scientist and a Nobel Laureate as measured by authorship in scientific literature.

Thus, if you have co-authored a paper with a Nobel Laureate, your Nobel number is 1.

Mine is 2.

PS: I saw Energy Secretary Steven Chu on CSPAN yesterday. He is a Nobel Laureate and co-authored a paper with my PhD advisor Ron Larson.

CiteULike: Export and clean BiBTeX file

As mentioned in a previous post, CiteULike lets you export citations in BiBTeX format, which is useful for including it in LaTeX documents. However, the BiBTeX entries it produces, contain a lot of metadata that I like to filter out.

One could do this manually, which is fine, but many of the cleaning operations are routine. As any self-respecting Linux user would say, it would be nice if one could automate at least part of the clean up process.

For example, a typical BiBTeX entry that CiteULike produces looks like:

@article{newman01,
abstract = {{We describe in detail an efficient algorithm for studying site or bond percolation on any lattice. The algorithm can measure an observable quantity in a percolation system for all values of the site or bond occupation probability from zero to one in an amount of time that scales linearly with the size of the system. We demonstrate our algorithm by using it to investigate a number of issues in percolation theory, including the position of the percolation transition for site percolation on the square lattice, the stretched exponential behavior of spanning probabilities away from the critical point, and the size of the giant component for site percolation on random graphs.}},
archivePrefix = {arXiv},
author = {Newman, M. E. J. and Ziff, R. M.},
citeulike-article-id = {3373958},
citeulike-linkout-0 = {http://arxiv.org/abs/cond-mat/0101295},
citeulike-linkout-1 = {http://arxiv.org/pdf/cond-mat/0101295},
citeulike-linkout-2 = {http://dx.doi.org/10.1103/PhysRevE.64.016706},
citeulike-linkout-3 = {http://link.aps.org/abstract/PRE/v64/i1/e016706},
citeulike-linkout-4 = {http://link.aps.org/pdf/PRE/v64/i1/e016706},
day = {8},
doi = {10.1103/PhysRevE.64.016706},
eprint = {cond-mat/0101295},
journal = {Physical Review E},
keywords = {cluster, fast, percolation},
month = {Jun},
number = {1},
pages = {016706+},
posted-at = {2011-01-12 21:22:08},
priority = {2},
publisher = {American Physical Society},
title = {{Fast Monte Carlo algorithm for site or bond percolation}},
url = {http://dx.doi.org/10.1103/PhysRevE.64.016706},
volume = {64},
year = {2001}
}

Typically, I like to get rid of all the "citeulike" tags, and irrelevant metadata such as priority, abstract etc. Additionally, I like to abbreviate journal titles, and replace author names with initials if necessary.

That is quite a bit.

I wrote a quick and dirty "sed" script, called "clean_citeulike" as below:

s/Physical Review/Phys. Rev./
s/Journal of Rheology/J. Rheol./
s/Rheologica Acta/Rheol. Acta/
s/The Journal of Chemical Physics/J. Chem. Phys./
s/Computer Physics Communications/Comp. Phys. Comm./
s/Macromolecular Theory Simulation/Macromol. Theory Simul./

/author/ s/, \([A-Z]\)[a-z]* /, \1. /g
/author/ s/, \([A-Z]\)[a-z]*}/, \1.}/g

/citeulike/ d
/keywords/ d
/posted-at/ d
/priority/ d
/publisher/ d
/abstract/ d
/month/ d
/url/ d
/day/ d
/issn/ d

Now, all I need to do is run the program on the "bib" file (which I call in.bib) according to

$ sed -f clean_citeulike in.bib

and I get something that looks like:

@article{newman01,
    archivePrefix = {arXiv},
    author = {Newman, M. E. J. and Ziff, R. M.},
    doi = {10.1103/PhysRevE.64.016706},
    eprint = {cond-mat/0101295},
    journal = {Phys. Rev. E},
    number = {1},
    pages = {016706+},
    title = {{Fast Monte Carlo algorithm for site or bond percolation}},
    volume = {64},
    year = {2001}
}

Not perfect perhaps, but much simpler to clean manually.

Undergrad Summer Internships: Email Spam?

It is funny that I received about half a dozen generic internship emails, since a recent post "Indian undergrads' internship request e-mails" appeared on nanopolitan. If you read the discussion around the topic (comments and links), it is clear that there are two categories of potential interns, the serious and the not-so-serious.

Let's dismiss the not-so-serious, and consider only the serious. At one point in time, I was one of them.

At that time, I was strongly conflicted between going to grad school or industry, with a bias towards the latter. Email was not as ubiquitous then (I think we had a quota of 300 extramural emails per semester, or something like that). Luckily, I spent an extremely enjoyable summer at HLRC, working on a computational project, after my sophomore year. I spent the following summer at an aromatics plant in Taloja. By the end of the second internship, it was clear to me that I wanted to go to grad school.

Clearly, I am indebted to those internships.

But now, my role has switched. I look at the process from an institutional standpoint. What did the institutions that gave me interships gain? My guess is that it let them "check me out" for possible future employment. In many ways, this is an efficient way to hire. The actual work was probably lost, shortly after I left.

From the standpoint of both, the student and the institution, the actual work accomplished over the internship is less important than the effect of that experience on future career/employment choices.

With that, let me get back to the "internship emails" I typically receive from IITians who want to work for about "8-12 weeks". The only motivation for a professor (in the US), that I can conjure, would be if he/she could recruit a good student for a subsequent MS or PhD. Anecdote is not data (and personal anecdote, less so), but I haven't seen a single internship prospect of this kind, materialize into a grad student. Surely counter-examples can be supplied, but if it were a great hiring tool, I suspect that it would naturally have been used more extensively (like it still is in industry).

CiteULike: Making Life Simpler

I've exclusively been using CiteULike to keep my bibliography straight, for almost four years now. If I had to make an ordered list of the most important web utilities (to me), it would probably float near the top.

It really has simplified a recurring feature of my Life.

Here is a YouTube video, which showcases some of its features.

Personally, I like it for the following reasons:

1. you can easily add a new reference to your library
2. you can tag, classify, and search for a paper in your library very quickly
3. you can add personal notes to a paper
4. you can upload a personal pdf of the paper to go with the reference
5. you can upload an annotated pdf (multiple pdfs are allowed), for papers you keep going back to again and again
6. you can export the citation in BiBTeX format
7. you can share papers with your group, or collaborators
8. you can use the "recommended papers" feature (I have not used this much)
9. It is free

Since one can archive papers online, they can easily be accessed from anywhere, and from any machine. For me, it has completely supplanted JabRef, which is a darn good program itself.

How is that journal title abbreviated?

Every once in a while you have to cite a paper from an unfamiliar journal. It can be frustrating not knowing how the journal title is abbreviated, although the internet has reduced that pain somewhat.

You can find how ISI abbreviates all the journal it indexes here. You can browse according to first letter, and use the search feature in your browser to find what you are looking for.

Recently, for example, the list helped me figure out that "IEEE Transactions on Visualization and Computer Graphics" was abbreviated as "IEEE T. Vis. Comput. Gr."