An Op-Ed from the NY Times (warning: paywall) suggests creating research and teaching tenure tracks and hire people for one or the other. This is an interesting proposal, and while the author Adam Grant marshals empirical evidence showing that the two skills are largely uncorrelated, as well as research on designing incentives, it seems that the social and economic barriers to implementing such a scheme are quite high.

Firstly, the economic. Grant-funded research faculty bring in big bucks (sometimes more modest bucks for pen-and-paper types) to the university. They overheads (55% at Rutgers, I think) on those grants help keep the university afloat, especially at places which don’t have huge endowments. Research in technology areas can also generate patents, startups, and other vehicles that bring money to the university coffers. This is an incentive for the university to push the research agenda first. Grant funding may be drying up, but it’s still a big money maker.

On the social barriers, it’s simply true in the US that as a society we don’t value teaching very highly. Sure, we complain about the quality of education and its price and so on, but the taxpayers and politicians are not willing to put their money where their mouth is. We see this in the low pay for K-12 teachers and the rise of the $5k-per-class adjunct at the university level. If a university finds that it’s doing well on research but poorly on teaching, the solution-on-the-cheap is to hire more adjuncts.

Of course, the proposal also represents a change, and institutionalized professionals hate change. For what it’s worth, I think it’s a good idea to have more tenure-track teaching positions. However, forcing a choice — research or teaching — is a terrible idea. I do like research, but part of the reason I want to be at a university is to engage with students through the classroom. I may not be the best teacher now, but I want to get better. A better, and more feasible, short-term solution would be to create more opportunities and support for teacher development within the university. This would strengthen the correlation between research and teaching success.

At Rutgers the faculty are unionized. Recently, the union reached a tentative agreement with the University regarding non-tenure track (NTT) faculty. The full text of the agreement is available now.

In the sciences and engineering, especially at research-focused universities, one often thinks of adjunct faculty as industry folks who come in and teach a class a semester or year. This stands in stark contrast to most departments in the humanities, where adjunct positions are (often) a way to dramatically underpay PhDs by paying them a mere $5k per course without benefits or even office space, sometimes. In the Boston area, the SEIU estimate is that “67 percent of the teaching faculty are not on the tenure track”. I don’t know how they estimated that number, and obviously the SEIU is a bit biased, but the number is certainly large.

Given the way the whole tenure system is going, any steps to provide more stability to adjunct contracts should be welcome. I think the short-term goal is to create more full-time instructional positions with benefits but without tenure. This agreement does something to address that. From an email I received:

Non-grant-funded NTT faculty who are successfully reappointed after six years of full-time service will have appointments of at least two years’ duration thereafter. Departments and decanal units will be required to develop, promulgate and post on their web sites clear criteria for appointment, reappointment, and promotion, and will also be required to provide all non-tenure track faculty with regular performance review and feedback.

Essentially, adjunct contracts were a bit of no-rules scenario before, and this is definitely a better situation.

The other big thing in the contract is to make the job titles more in line with other institutions. There are now 5 classes of non-tenure track faculty: Teaching, Professional Practice, Librarian, Clinical and Research. The first three are new. I’m not sure how the NTT body as a whole feels about this, and in a sense this approach is a capitulation to the trend of having fewer tenure-track faculty, but I think it’s much better than what we have now.

It’s been a busy January — I finished up a family vacation, moved into a new apartment, helped run the MIT Mystery Hunt, started teaching at Rutgers, and had two conference deadlines back to back. One of my goals for the year is to blog a bit more regularly — I owe some follow-up to my discussion of the MAP perturbation work, which I will be talking about at ITA.

In the meantime, however, one of the big tasks in January is graduate admissions. I helped out with admissions at Berkeley for 4 years, so I’m familiar with reviewing the (mostly international) transcripts, but the level of detail in transcript reporting varies widely. The same is true for letters of recommendation. I’m sure this is culturally mediated, but some recommenders write 1-2 sentences, and some write paeans. This makes calibrating across institutions very difficult. While the tails of the distribution are easy to assess, decisions about the middle are a bit tougher.

Rutgers, like many engineering school across the country, has a large Masters program. Such programs serve as a gateway for foreign engineers to enter the US workforce — it’s much easier to get hired if you’re already here. It’s also makes money for the university, since most students pay their own way. In that regards, Rutgers is a pretty good deal, being a state school. However, it also means making admissions decisions about the middle of the distribution. What one wants is to estimate the probability an applicant will succeed in their Masters level classes.

It’s a challenging problem — without being able to get the same level of detail about the candidates, their schools, and how their recommenders feel about their chances, one is left with a kind of robust estimation problem with a woefully underspecified likelihood. I’ve heard some people (at other schools) discuss GPA cutoffs, but those aren’t calibrated either. More detail about a particular individual doesn’t really help. I think it’s a systemic problem with how graduate applications work in larger programs; our model now appears better suited to small departments with moderate cohort sizes.

I’m at EPFL right now on my way back from a trip to India. My last work-related stop there was the Tata Institute of Fundamental Research, where I am working with Vinod Prabhakaran on some follow-up work to our ISIT paper this year on correlated sampling. TIFR has a lovely campus in Navy Nagar, right next to the ocean in the south of Mumbai. It’s a short walk across the lawn to the ocean:

The beach at TIFR, looking north to the haze-obscured skyline of Mumbai

The beach at TIFR, looking north to the haze-obscured skyline of Mumbai

The grounds themselves are pretty rigorously landscaped and manicured:

The main building seen across the lawn in front of the ocean.

The main building seen across the lawn in front of the ocean.

Earlier in my trip I visited IIT-Madras, hosted by Andrew Thangaraj and Radha Krishna Ganti and IIT-Bombay, where I was working with Bikash Dey on extensions to some of our AVCs-with-delay work. It’s been a great trip and it’s nice to visit so many different schools. The challenges facing Indian higher education are very different than those in the US, and it’s a good change of perspective from my usual US-centric view of things. Maybe I’ll write more on that later.

But for now, I wanted to get back to some actual technical stuff, so I thought I’d mention something that came up in the course of my discussions with Vinod today. For a joint distribution P(X,Y), Wyner defined the common information in the the following way:

\mathsf{CI}(X;Y) = \min_{X--U--Y} I(XY ; U)

One fun fact about the common information is that it’s larger than the mutual information, so I(X;Y) \le \mathsf{CI}(X;Y). One natural question that popped up as part of a calculation we had to do was whether for doubly-symmetric binary sources we could have a bound like

\mathsf{CI}(X;Y) \le \alpha I(X;Y)

for some “nice” \alpha. In particular, it would have been nice for us if the inequality held with \alpha = 2 but that turns out to not be the case.

Suppose (X,Y) and are a doubly-symmetric binary source, where Y is formed from X \sim \mathsf{Bern}(1/2) by passing it through a binary symmetric channel (BSC) with crossover probability \alpha. Clearly the mutual information is I(X;Y) = 1 - h(\alpha). For the common information, we turn to Wyner’s paper, which says \mathsf{CI}(X;Y) = 1 + h(\alpha) - 2 h( \frac{1}{2}( 1 - \sqrt{1 - 2 \alpha} ) ), which is a bit of a weird expression. Plotting the two for \alpha between 0 and 1/2 we get:

Mutual and Common Information for a DSBS

Mutual and Common Information for a DSBS

If we plot the ratio \mathsf{CI}(X;Y)/I(X;Y) versus \alpha, we get the following:

Ratio of Common to Mutual Information for a DSBS

Ratio of Common to Mutual Information for a DSBS


The ratio blows up! So not only is Common Information larger than mutual information, it can be an arbitrarily large factor larger than the mutual information.

It might seem like this is bad news for us, but it just made the problem we’re working on more interesting. If we get any results on that I might be less engimatic and blog about it.

Avleen Bijral, one of the students here at TTIC, sent me a link to a pretty cool browser plugin for GMail called GMail TeX. Basically it lets you send LaTeX formatted emails via gmail. This can be quite a time saver for the reader (but maybe not the writer) if you’re remotely collaborating on some project (which I so often am these days, it seems). It’s supported on a wide variety of browsers and seems worth checking out!

I got an email from the Rutgers ECE department about a new scam that has been preying on international students. Some readers of the blog may wish to be aware of this.

Scammers posing as immigration officials – United States Citizenship and Immigration Services (USCIS) officers, Department of State (DOS) officials, or local policemen are contacting international students, & scholars. They may ask for your personal information (such as SSN, passport number, credit card information), identify false problems with your immigration record, and ask for payment to correct the record.

These scams can be very sophisticated with a USCIS like number appearing on your caller ID, the caller knowing a lot of your personal information and so on. Here are a few pointers to bear in mind if you receive this kind of a fake “immigration” call:

  • Don’t wire/pay any money. USCIS will never call someone to ask for any form of payment over the phone.
  • Just hang up and don’t call back.
  • Call the real USCIS National Customer Service Center at 1-800-375-5283 to report the issue.
  • Never give out your personal information. No matter who a caller claims to be, don’t give him/her your I-94 number, “A” number, visa control number or any other personal information. Hang up and call the real USCIS.

Apparently Rutgers had an incident involving a postdoc, where the caller posed as a police officer “and informed the scholar that he had immigration problems. This female caller had the scholar’s home address, cell phone number and SSN number. She informed the scholar that the problem can be resolved if he could make an immediate payment.”

More information can be found at the USCIS website.

A map of racial segregation in the US.

Vi Hart explains serial music (h/t Jim CaJacob).

More adventures in trolling scam journals with bogus papers (h/t my father).

Brighten does some number crunching on his research notebook.

Jerry takes “disruptive innovation” to task.

Vladimir Horowitz plays a concert at the Carter White House. Also Jim Lehrer looks very young. The program (as cribbed from YouTube)

  • The Star-Spangled Banner
  • Chopin: Sonata in B-flat minor, opus 35, n°2
  • Chopin: Waltz in a minor, opus 34, n°2
  • Chopin: Waltz in C-sharp minor, opus 64, n° 2
  • Chopin: Polonaise in A-flat major, opus 53 ,Héroïque
  • Schumann: Träumerei, Kinderszene n°7
  • Rachmaninoff: Polka de W.R
  • Horowitz: Variations on a theme from Bizet’s Carmen

The Simons Institute is going strong at Berkeley now. Moritz Hardt has some opinions about what CS theory should say about “big data,” and how it might be require some adjustments to ways of thinking. Suresh responds in part by pointing out some of the successes of the past.

John Holbo is reading Appiah and makes me want to read Appiah. My book queue is already a bit long though…

An important thing to realize about performance art that makes a splash is that it can be often exploitative.

Mimosa shows us what she sees.

I really love language, but my desire to be idiomatic often runs athwart to the goals of scholarly communication. Especially when motivating a paper, I enjoy writing sentences such as this:

In the data-rich setting, at first blush it appears that learning algorithms can enjoy both low privacy risk and high utility.

(Yes, I know it’s passive). However, consider the poor graduate student for whom English is a second (or third) language – this sentence is needlessly confusing for them. If we are really honest, this is the dominant group of people who will actually read the paper, so if I am writing with my “target audience” in mind, I should eschew idioms, literary allusions, and the like. Technical papers should be written in a technical and global English (as opposed to a specific canonized World English).

Nevertheless, I want to write papers in a “writerly” way; I want to use the fact that my chosen career is one of constant writing as an opportunity to improve my communication skills, but I also want to play, to exploit the richness of English, and even to slip in the occasional pun. Am I a linguistic chauvinist? Unlike mathematicians, I never had to learn to read scholarly works in another language, so I have the luxury of lazily indulging my fantasies of being an Author.

When I review papers I often have many comments about grammatical issues, but perhaps in a global English of scholarly communication it doesn’t matter as long as the argument is intelligible. Do we need so many articles? Is consistent tense really that important? I think so, but I don’t have a philosophically consistent argument for what may appear, in situ, to be a prescriptivist attitude.

The cynical side of me says that nobody reads papers anyway, so there is no point in worrying about these issues or even spending time on the literary aspects of technical papers. Cynicism has never been very nourishing for me, though, so I am hoping for an alternative…

One big difference between reviewing for conferences like NIPS/ICML and ISIT is that there is a “discussion” period between the reviewers and the Area Chair. These discussions are not anonymized, so you know who the other reviewers are and you can also read their reviews. This leads to a little privacy problem — A and B may be reviewing the same paper P, but A may be an author on a paper Q which is also being reviewed by B. Because A will have access to the text of B’s reviews on P and Q, they can (often) unmask B’s authorship of the review on Q simply by looking at the formatting of the reviews (are bullet points dashes or asterisks, do they give numbered points, are there “sections” to the review, etc). This seems to violate the spirit of anonymous review, which is perhaps why some have suggested that reviewing be unblinded (at least after acceptance).

The extent to which all of this matter is of course a product of the how fast the machine learning literature has grown and the highly competitive nature of the “top tier conferences.” Because the acceptance rate is so low, the reviewing process can appear to be “arbitrary” (read: subjective) and so questions of both review quality and author/review anonymity impact possible biases. However, if aim of double-blind reviewing is to reduce bias, then shouldn’t the discussions also be anonymized?

Michael Eisen gave a talk at the Commonwealth Club in San Francisco recently. Eisen is the founder of the Public Library of Science (PLoS), which publishes a large number of open-access journals in the biosciences, including the amazingly named PLoS Neglected Tropical Diseases. His remarks begin with the background on the “stranglehold existing journals have on academic publishing.” But he also has this throwaway remark:

One last bit of introduction. I am a scientist, and so, for the rest of this talk, I am going to focus on the scientific literature. But everything I will say holds equally true for other areas of scholarship.

This is simply not true — one cannot generalize from one domain of scholarship to all areas of scholarship. In fact, it is in the differences between dysfunctions of academic communication across areas that we can understand what to do about it. It’s not just that this is a lazy generalization, but rather that the as Eisen paints it, in science the journals are more or less separate from the researchers and parasitic entities. As such, there are no reasons that people should publish with academic publishers except for some kind of Stockholm syndrome.

In electrical engineering and computer science the situation is a bit different. IEEE and ACM are not just publishing conglomerates, but are supposed to be the professional societies for their respective fields. People gain professional brownie points for winning IEEE or ACM awards, they can “level up” by becoming Senior Members, and so on. Because disciplinary boundaries are a little more fluid, there are several different Transactions in which a given researcher may publish. At least on paper, IEEE and ACM are not-for-profit corporations. This is not to say that engineering researchers are not suffering from a Stockholm syndrome effect with these professional societies. It’s just that the nature of the beast is different, and when we talk about how IEEExplore or ACM Digital Library is overpriced, that critique should be coupled with one of IEEE’s policy requiring conferences to have a certain profit level. These things are related.

The second issue I had is with Eisen’s proposed solution:

There should be no journal hierarchy, only broad journals like PLOS ONE. When papers are submitted to these journals, they should be immediately made available for free online – clearly marked to indicate that they have not yet been reviewed, but there to be used by people in the field capable of deciding on their own if the work is sound and important.

So… this already exists for large portions of mathematics and mathematical sciences and engineering in the form of ArXiV. The added suggestion is a layer of peer-review on top, so maybe ArXiV plus a StackExchange thing. Perhaps this notion is a radical shift for life sciences where Science and Nature are so dominant, but what I learn myself from looking at the ArXiV RSS feed is that the first drafts of papers that get put up there are usually not the clearest exposition of the work, and without some kind of community sanction (in the form of rejection), there is little incentive for authors to actually go back and make a cleaner version of their proof. If someone has a good idea or result but a confusing presentation they are not going to get downvoted. If someone is famous they are unlikely to get downvoted.

In the end what PLoS ONE and the ArXiV-only model for publishing does is reify and retrench the existing tit-for-tat “clubbiness” that exists in smaller academic communities. In a lot of CS conferences reviewing is double-blind as a way to address this very issue. When someone says “all academic publishing has the same problems” this misses the point, because the problems is not always with publishing but with communication. We need to understand the how the way we communicate the products scholarly knowledge is broken. In some fields, I bet you could argue that papers are inefficient and bad ways of communicating results. In this sense, academic publishing and its rapacious nature are just symptoms of a larger problem.

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