ResearchGate: spam scam, or…?

I’ve been getting fairly regular automated emails lately from ResearchGate, which has pull-quotes from Forbes and NPR saying it’s changing the way we do research blah blah blah. However, all empirical reports I have heard indicate that once you join, it repeatedly spams all of your co-authors with requests to join, which makes it feel a bit more like Heaven’s Gate.

On a less grim note, the site’s promise to make your research “more visible” sounds a bit like SEO spam. Given the existence of Google Scholar, which is run by the SE that one would like to O, it seems slightly implausible.

Any readers want to weigh in on whether ResearchGate has been useful to them? Or is this mostly for people who don’t know how to make their own homepage with their papers on it (which is probably most faculty).

Some LaTeX hacks for writing proposals

I just submitted my CAREER proposal on Monday, and now that the endless revision process is over, I wanted to write a post about what I learned about proposal writing. But that seems like hubris, since I have no idea if the thing will get funded, so what do I know? Besides, everything I learned was from the valuable feedback I got from those who very kindly read my previous drafts. So instead of writing about How To Sell Your Idea, I figured I would write about some cute LaTeX hacks that I came across, most of them via the very useful TeX-LaTeX Stack Exchange.

If anyone else has some useful hacks, feel free to leave them in the comments!

Saving Space

One of the big problems in NSF proposal writing is that there’s a hard limit on the number of pages (not the number of words), so if you’re at the edge, there’s a lot of “oops, two lines over” hacking to be done towards the end.

  • \usepackage{times} \usepackage{mathptmx}: The typeface for your proposal makes a big difference in space. Computer Modern is a bit easier to read since there’s more whitespace, but Times shaved a whole page off of my proposal. The NSF Grant Proposal Guidelines has the list of approved formatting. It seems standard for NIH proposals to use 11pt Arial but that makes me want to gouge my eyes out. Know thy reviewers, is what I would say: keep in mind what’s standard for the solicitation and don’t make the proposal so dense as to be unreadable. NB: Apparently the times package is deprecated (see comments).
  • \usepackage{titlesec}. This package lets you control the spacing around your titles and subtitles like this:


    \titlespacing\section{0pt}{10pt plus 2pt minus 2pt}{2pt plus 2pt minus 2pt}
    \titlespacing\subsection{0pt}{8pt plus 2pt minus 2pt}{2pt plus 2pt minus 2pt}

    See this post for more details, but basically it’s \titlespacing{command}{left spacing}{before spacing}{after spacing}. This is handy because there’s a lot of empty space around titles/subtitles and it’s an easy way to trim a few lines while making sure things don’t get too cramped/ugly.

  • \usepackage{enumitem}: This package lets you control the spacing around your enumerate lists. The package has a lot of options but one that may be handy is \setlist{nosep} which removes the space around the list items. This actually makes things a little ugly, I think, but bulleted lists are helpful to the reviewer and they also take a little more space, so this lets you control the tradeoff. Another thing that is handy to control is the left margin: \setlist[itemize,1]{leftmargin=20pt}.
  • \usepackage{savetrees}: Prasad says it’s great, but I didn’t really use it. YMMV.

Customizations

  • Sometimes it’s handy to have a new theorem environment for Specific Aims or Open Problems or what-have-you. The problem is (as usual) that the theorem environment by itself puts in extra space and isn’t particularly customizable. So one option is to define a new theorem style:


    \newtheoremstyle{mystyle}% name
    {5pt}%Space above
    {5pt}%Space below
    {\itshape}% Body font
    {5pt}%Indent amount
    {\bfseries}% Theorem head font
    {:}%Punctuation after theorem head
    {4pt}%Space after theorem head 2
    {}%Theorem head spec (can be left empty, meaning ‘normal’)

    \theoremstyle{mystyle}
    \newtheorem{specaim}{Specific Aim}

  • Another handy hack is to make a different citation command to use for your own work that will then appear in a different color than normal citations if you use \usepackage[colorlinks]{hyperref}. I learned how to do this by asking a question on the stack exchange.


    \makeatletter
    \newcommand*{\citeme}{%
    \begingroup
    \hypersetup{citecolor=red}%
    \@ifnextchar[\citeme@opt\citeme@
    }
    \def\citeme@opt[#1]#2{%
    \cite[{#1}]{#2}%
    \endgroup
    }
    \newcommand*{\citeme@}[1]{%
    \cite{#1}%
    \endgroup
    }
    \makeatother

  • The hyperref package also creates internal links to equations and Figures (if you label them) and so on, but the link is usually just the number of the label, so you have to click on “1” instead of “Figure 1″ being the link. One way to improve this is to make a custom reference command:


    \newcommand{\fref}[2]{\hyperref[#2]{#1 \ref*{#2}}}

    So now you can write \fref{Figure}{fig:myfig} to get “Figure 1″ to be clickable.

  • You can also customize the colors for hyperlinks:


    \hypersetup{
    colorlinks,
    citecolor=blue,
    linkcolor=magenta,
    urlcolor=MidnightBlue}

  • Depending on your SRO, they may ask you to deactivate URLs in the references section. I had to ask to figure this out, but basically putting \let\url\nolinkurl before the bibliography seemed to work…

ICML 2014: thoughts on the format

This is my first time at ICML, and every paper here has a talk and a poster. It’s a lot of work to prepare, but one nice benefit is that because my poster had to be done before I left, the talk was also pretty much done at the same time, modulo minor tweaks. Having to be ready early means less last-minute preparations and lower-stress at the conference overall. Another plus is that some talks are probably better as posters and some posters are probably better as talks, so the two modes of presentation gives a diversity to the delivery process. Some people also prefer talks to posters or vice-versa, so that’s good for them as well. Finally, the conference has 6 parallel tracks, so knowing that there’s a poster takes some of the stress out of deciding which session to attend — you can always catch the poster if you missed the talk.

The major minus is time. Sessions run from 8:30 to 6 and then posters run from 7 to 11 PM — it’s overwhelming! You can easily spend the entire conference at talks and then at posters, resulting in a brain overload. This also leaves less time for chatting and catching up with colleagues over dinner, starting up new research ideas or continuing ongoing projects in person, and the informal communication that happens at conferences. People do make time for that, but the format less conducive to it, or so it appeared to me. I ended up taking time off a bit during the sessions to take a walk around the Olympic park and have a chat, and I saw others leaving to do some sightseeing, so perhaps I am adhering to the schedule too much.

It’s interesting how different the modes of conference/social research communication are across research disciplines. I’ve yet to go to ICASSP or ICC, and while I have been to a medical informatics conference once, I haven’t gone to a Big Science conference or the joint meetings for mathematics or statistics. I imagine the whole purpose and format of those is completely different, and it makes me wonder if the particular formats of machine learning conferences are intentional: since there is rarely an extended/journal version of the paper, the conference is the only opportunity for attendees to really buttonhole the author and ask questions about details that are missing from the paper. Perhaps maximizing author exposure is a means to an end.

Line-item cost of one student-year on a grant?

I am in the process of writing some proposals and am encountering the fun task of generating budgets for those proposals. Rutgers, like many cash-strapped schools, imposes a hefty “overhead” charge on federal grants (the so-called indirect costs) amounting to something like more than 50% of the value of the grant. Since I’m primarily a theory guy, the largest line item on any grant I write is generally a graduate students. With stipend, tuition, fees, and benefits, a calendar-year appointment for a graduate student costs around $90k, factoring indirect costs. Given that an NSF Small award caps out at $500k, it’s quite difficult to support more than one student for a small grant. This in turn limits the scope of research one can propose — it’s all fine and well to say there are 15 journal papers’ worth of results stemming from your great ideas, but 3-4 student years is probably not enough to make that happen.

I know some schools offer a tuition break for RAs/GSRs, but I am not sure how prevalent this practice is. So I put it to the readers of the blog: what is the line-item cost to support a graduate student for one year (without travel etc.) at your institution?

Mental health in graduate school

I recently posted a link to an article on mental health in graduate school on Facebook (via a grad school friend of mine), and it sparked a fair bit of discussion there. The article is worth reading, and I am sure will echo with many of the readers. The discussion veered towards particularities of graduate school pressures in STEM, and the contributing factors to mental stress that are driven by funding structures and the advisor/student relationship. The starting point comes from this part of the article:

In this advisor-advisee arrangement, the student trades her labor as a researcher for the advisor’s mentorship and, ultimately, the advisor’s approval of her degree before she can graduate. For students seeking an academic position after graduate school, an advisor’s letter of recommendation can be the difference between landing a job and being left out in the cold, a harsh reality given today’s sparse academic job market. All of these factors mean that the faculty advisors hold tremendous power in the advisor-advisee relationships. They are the gatekeepers of success in the graduate endeavor.

This notion of “trading labor for mentorship” is most directly monetized in grant-funded fields like engineering, where graduate students are “working in the lab” on a project that is (hopefully) related to their thesis topic. In some cases, this works out fine, but in others, the research for the grant-relevant project does not contribute directly to their thesis. For funding agencies which want “deliverables,” this pressure to produce results on schedule creates a tension. The advisor becomes a boss.

Some of the points raised in the discussion on Facebook seemed important to bring out to a wider audience. One suggestion is to disentangle NSF support for projects and research from grad student salaries. So students could apply for NSF support and then they take their funding with them to find an advisor. In STEM this would be difficult, given the large number of international students who would not be qualified for such support, but it does give some power to students to walk away from a bad situation and more incentives for PIs to be more mentors than bosses. I am not entirely convinced it would help in terms of mental health though — students need more and better mentoring, not just the means to walk away. Also, Roy pointed out, having the student and advisor both convinced that a problem is important and solvable creates a shared commitment that helps students feel less isolated. For postdocs, though, this model would be a significant improvement over the status quo. Right now, there is almost no consensus on what a postdoc should be, and I’ve seen postdoc jobs that range from factotum to co-PI.

When one is on the other side (post-PhD), it’s tempting to say that grad school would have been easier if I had been a bit more organized or had better time-management skills. Perhaps the difficulties one has can be solved with “one weird trick.” I think that’s terribly naïve. As advisors, we definitely can do things to help students learn to work better — that’s the transition from being a student to being a researcher. But the notion that depression comes about as a result of simply not being productive enough, or feeling behind, or any other “outcomes”-based reason, misses the environmental and social factors that are equally important.

Graduate research is often very isolating. Perhaps some STEM students actually enjoy this kind of solitary work, but generalizing is dangerous. Having a grad student social organization, weekly happy hour, softball league, or other “outlet” isn’t enough. I used my startup funds to help buy a table-tennis table for my department at Rutgers, and while the students seem pretty happy about it, it’s not actually creating a community. One important question to ask is how the faculty and the department can help create and support that kind of community so that it can go on its own, organically.

In a department like mine, the majority of graduate students are international, and have a host of other stressors about being in a new (and often much more expensive) country. Using mental health resources may not be normalized in their home country or culture. Regardless of where they are from however, the big challenge is this:

…awareness of the existing resources among the graduate student population remains frustratingly low, due in part to the insular nature of traditional academic departments. A broader culture of wellness may prove even more elusive in the face of a rigidly hierarchical academic culture that often rewards drive and sacrifice without encouraging balance. In this climate, graduate student mental health advocates—students, staff, and administrators—face an uphill struggle in the years to come. The consequences of this struggle tear at the very fabric of the academic experience and suggest fundamental misalignment of priorities.

It’s only a misalignment of priorities if we don’t interrogate our priorities. This isn’t two trains crashing into each other, but it does require a “structural” recognition that graduate students are a part of the family, as it were, and treating them as such.

Linkage

My friend Cynthia her friends have a tumblr on inclusivity in STEM. See also the quarterly Model View Culture, which I think I had seen an article from but didn’t realize it was a whole journal. Thanks to Lily Irani for the link.

This list of streamable Errol Morris movies is dangerous.

Maybe when I am in Bangalore I will get to learn more about The Ugly Indian.

How Chicago’s neighborhoods got their names. It does not explain Mr. Wicker’s crazy hat though.

Alex Smola gave a talk at DIMACS recently where he talked about the alias method for generating biased random variables. I think he even snagged the figures from that website as well…

A proposal for restructuring tenure

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.

Non-tenure track faculty at Rutgers get a contract

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.

Starting up, and some thoughts on admissions

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.

Bounds between common information and mutual information

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.