I’m happy to announce that in January 2014 I will be joining the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey, as an Assistant Professor.

My department chair sent out a recent notice from the NSF about the impact of the sequestration order on the NSF awards.

At NSF, the major impact of sequestration will be seen in reductions to the number of new research grants and cooperative agreements awarded in FY 2013. We anticipate that the total number of new research grants will be reduced by approximately 1,000.

In FY2011 the NSF funded 11,185 proposals, so that’s an 8.94% reduction. Yikes.

As I’ve gotten farther along in this whole research career, I’ve found it more and more difficult to figure out the optimal way to balance the different things one does at a conference :

• Going to talks. This is ostensibly the point of the conference. It’s impossible to read all of the papers that are out there and a talk is a fast way to get the gist of a bunch of papers or learn about a new problem in less time than it takes to really read and digest the paper. We’re social creatures so it’s more natural to get information this way.
• Meeting collaborators to talk about research problems. I have lots of collaborators who are outside TTI and a conference is a good chance to catch up with them face-to-face, actually sit down and hammer out some details of a problem, or work on a new problem with a (potential) new collaborator. Time sitting over a notepad is time not spent in talks, though.
• Professional networking. I’m on the job market now, and it’s important to at least chat casually with people about your research, what you think is exciting your future plans, and the like. This is sometimes the “real” point of conferences.
• Social networking. Sometimes conferences are the only times I get to see my friends from grad school, and in a sense your professional peers are the only people who “get” your crazy obsession with esoteric problem $P$ and like to get a beer with you.

So the question for the readership : how do you decide the right balance for yourself? Do you go in with a plan to see at least N talks or a certain set $S$ of talks, or are you open to just huddling in the corner with a notepad?

I wrote this post in an attempt to procrastinate about ITA blogging, which I will get to in a bit. I went to far fewer talks than I expected to this year, but I’ll write about ‘em later.

Postdoctoral Research Fellow ‐ Science of Information

Position Description: The Center for Science of Information (CSoI) invites nominations and applications for its CSoI Research Fellows program. The Fellows program seeks to identify and groom intellectual leaders to shape the emerging field of Science of Information and its diverse applications. The program provides opportunities for dynamic individuals to interact with premier research groups and individuals worldwide. Researchers in the Center for Science on Information have received the top awards in their fields (Rolf Nevanlinna Prize, Nobel Prize, Claude Shannon Award, and the Alan Turing Award). Research fellows will have opportunities to work in an enriching interdisciplinary environment, with significant potential for broad impact in terms of technology development, industry involvement, and educational initiatives. Successful candidates are expected to work with Center Researchers at two or more of the participating institutions. Fellows are appointed for an initial term of one year, and may be extended contingent on satisfactory performance.

Qualifications:

• Candidate must have received a Ph.D. degree in a field related to the Science of Information within the past five years from an accredited college or university.
• Candidates must have a track record of research contributions in information theory and/or associated areas related to the research agenda of the Center.
• Excellent oral and written communication skills.

How to Apply: Applications should include:

• A complete CV including education, employment history, and publications;
• Statement of research interests, along with a short list of possible CSoI faculty they would be interested in working with; and
• Names and contact information of at least three references

Please email nominations or applications to:
Search Committee Chair
CSoI Research Fellows Program
csoi@soihub.org

Applications will be accepted until Fellows have been selected. Review of applications will begin March 15, 2013.

About the Center for Science of Information:
CSoI is a NSF‐funded Science and Technology Center (STC) whose mission is to advance science and technology through a new quantitative understanding of the representation, communication, and processing of information in biological, physical, social, and engineered systems. The Center is a collaboration among computer scientists, mathematicians, engineers, biologists, and economists at several institutions including: Bryn Mawr, Howard, MIT, Princeton, Purdue, Stanford, Texas A&M, UC‐Berkeley, UC‐San Diego, and UIUC. Please visit the CSoI website for more information about the Center and current research activities.

The Center for Science of Information (CSoI) is committed to diversity and equality of opportunity. Applications from women, minorities, and persons with disabilities are encouraged.

I’ve had to do a lot of explaining about my current position and institution since moving here, especially when I go visit ECE departments. So I figured I might use the blog to give a quick rundown of the job. I’m a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus.

• The Toyota Technological Institute at Chicago is a branch of the Toyota Technological Institute in Nagoya, Japan. Their website is a little slow to load, but the Wikipedia entry has more quick facts. TTI-Japan was founded through an endowment from the Toyota Motor Corporation in 1981 (so it’s younger than me). The Toyota Motor Corporation is not my employer, although some executives are on the board of the school.
• I do not work for Toyota. My research has nothing to do with cars. At least not intentionally.
• TTI-Chicago is basically a stand-alone computer science department and was started in 2003. It only has graduate students and grants its own degrees. It happens to be located on the University of Chicago campus — we rent two floors of a building which also contains the IT services. Classes at TTI are cross-listed with the University of Chicago — students at TTI take classes at UChicago and students at UChicago take classes at TTI.
• I get an “affiliate” card for UChicago which lets me use the library and stuff. It’s great to have a library there, but since UChicago has no engineering, my access to IEEExplore is a bit limited.
• The research at TTI-Chicago is mostly in machine learning, computer vision, speech processing, computational biology, and CS theory. This makes me a bit of an odd-one-out, but I have been doing more machine learning lately. It’s fun learning new perspectives on things and new problems.
• The Research Assistant Professor position at TTI-Chicago is a 3-year position (some people have stayed for 4) which pays a 9 month salary (out of general institute funds) and gives a yearly budget for research expenses like travel/conferences and experimental costs (e.g. for Mechanical Turk or Amazon EC2). It’s not a “soft money” position but people are free to raise their summer salary through grants (like I did) or by taking a visiting position elsewhere for part of the year. I do not have to teach but can offer to teach classes or help teach classes
• There are tenure-track faculty at TTI, and it’s the same tenure deal as elsewhere. Their teaching load is one quarter per year (that should make people jealous).
• There are graduate students here, but not a whole lot of them. I can’t directly supervise graduate students but I can work with them on research projects. I’m starting to work with one student here and I’m pretty excited about our project.

The ECE department at The University of Texas at Austin seeks highly qualified candidates for postdoctoral fellowship positions, lasting up to two years, in the information sciences, broadly defined. Applicants should have, or be close to completing, a PhD in ECE, CS, Math, Statistics or related fields.

I just got the CRA newsletter, and it had a link to a document on best practices for mentoring postdocs:

… data from the Computing Research Association’s (CRA) annual Taulbee Survey indicate that the numbers of recent Ph.D.s pursuing postdocs following graduate school soared from 60 in 1998 to 249 in 2011 (three-year rolling averages), an increase of 315 percent during this period. Because research organizations are suddenly channeling many more young researchers into these positions, it is incumbent upon us as a community to have a clear understanding of the best practices associated with pursuing, hosting, and nurturing postdocs.

I think you’d find the same numbers in EE as well. This report relies a fair bit on the National Academies report, which is a little out of date and I thought very skewed towards those in the sciences. Engineering is a different beast (and perhaps computer science an even more different beast), so I think that while there are some universal issues, the emphasis and importance of different aspects varies across fields quite a bit. For example, the NA report focuses quite a bit on fairness in recruiting which are predicated on the postdoc being a “normal” thing to do. By contrast, in many engineering fields postdoc positions are relatively new and there’s an opportunity to define what the position means and what it is for (i.e. not a person you can pay cheaply to supervise your graduate students for you).

A few weeks (!) ago I was talking with an anthropologist friend of mine about how different fields have different modes of communicating research “findings” in the conference setting. Some places people just read their paper out loud, others have slide presentations, yet others have posters, and I imagine some people do blackboard talks. Of course, conferences have many purposes — schmoozing, job hunting, academic political wrangling, and so on. What is unclear to me is why particular academic communities have settled on particular norms for presenting their work.

One axis along which to understand this might be the degree to which the presentation of the paper is an advertisement for the written paper. In many humanities conferences, people simply read their paper out loud. You’d think that theater researchers would be able to make a more… dramatic reading of their work, but you’d be wrong much of the time. It’s very hard to sit and listen and follow a jargon-heavy analysis of something that you probably have never read about (e.g. turn of the century commercial theater in Prague), and in some sense I feel that the talk as an advertisement for the paper is minimal here.

On the other hand, a poster session maximizes the “advertisement of the paper” aspect. People stand there for 5 minutes while you explain the ideas in the paper, and if seems sufficiently interesting then they will go and read the actual paper. A difference here between the model in the humanities is that there is a paper in the proceedings, while in humanities conferences this is not necessarily the case.

Slide presentations are somewhere in the middle — I often go to a talk at a conference and think “well, now I don’t need to read the paper.” These are the trickiest because the audience is captive but you cannot give them the full story. It’s more of a method for luring already-interested people into wanting to read the paper rather than the browsing model of a poster session.

However, even this “advertisement” categorization raises the question of why we have poster sessions, slide presentations, and paper readings. Are these the best way to present the research in those fields? Should we have more posters at ISIT and fewer talks (more like NIPS)? Should NIPS have more parallel sessions to reflect the spread of interest in the “community?” Should anthropology conferences have each panelist give an 8 minute slide presentation followed by real discussion?

I missed ITW in Lausanne this year, but I heard that they mixed up the format to great success. More posters and fewer talks meant more interaction and more discussion. I think more experimenting could be good — maybe some talks should be given as chalk talks with no slides!

I was recently reading a paper on ArXiV that is from the VLDB 2012 conference:

Functional Mechanism: Regression Analysis under Differential Privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, Marianne Winslett

The idea of the paper is to make a differentially private approximation to an optimization by perturbing a Taylor series expansion of the objective function. Which is an interesting idea at that. However, what caught my eye was how they referred to an earlier paper of mine (with Kamalika Chaudhuri and Claire Monteleoni) on differentially private empirical risk minimization. What we did in that paper was look at the problem of training classifiers via ERM and the particular examples we used for experiments were logistic regression and SVM.

In the VLDB paper, the authors write:

The algorithm, however, is inapplicable for standard logistic regression, as the cost function of logistic regression does not satisfy convexity requirement. Instead, Chaudhuri et al. demonstrate that their algorithm can address a non-standard type of logistic regression with a modified input (see Section 3 for details). Nevertheless, it is unclear whether the modified logistic regression is useful in practice.

This is just incorrect. What we look at is a fairly standard formulation of logistic regression with labels in {-1,+1}, and do the standard machine learning approach, namely regularized empirical risk minimization. The objective function is, in fact, convex. We further do experiments using that algorithm on standard datasets. Perhaps the empirical performance was not as great as they might like, but then they should make a claim of some sort instead of saying it’s “unclear.”

They further claim:

In particular, they assume that for each tuple t_i, its value on Y is not a boolean value that indicates whether t_i satisfies certain condition; instead, they assume y_i equals the probability that a condition is satisfied given x_i… Furthermore, Chaudhuri et al.’s method cannot be applied on datasets where Y is a boolean attribute…

Firstly, we never make this “assumption.” Secondly, we do experiments using that algorithm on standard datasets where the label is binary. Reading this description was like being in a weird dream-world in which statements are made up and attributed to you.

Naturally, I was a bit confused about this rather blatant misrepresentation of our paper, so I emailed the authors, who essentially said that they were confused by the description in our paper and that more technical definitions are needed because we are from “different communities.” They claimed that they emailed questions about it but we could not find any such emails. Sure, sometimes papers can be confusing if they are out of your area, but to take “I don’t understand X” to “let me make things up about X” requires a level of gumption that I don’t think I could really muster.

In a sense, the publication incentives are stacked in favor of this kind of misrepresentation. VLDB is a very selective conference, so in order to make your contribution seem like a big deal, you have to make it seem that alternative approaches to the problem are severely lacking. However, rather than making a case against the empirical performance of our method, this paper just invented “facts” about our paper. The sad thing is that it seems completely unnecessary, since their method is quite different.

Snuff [Terry Pratchett] : this was standard Discworld stuff, but I found it a little below-average. I’m finicky that way though.

Snakes Can’t Run [Ed Lin] : a follow-up to This Is A Bust, this book is about human smuggling in New York Chinatown in the 70s. Recommended if you like thing Asian and mysterious.

Shark’s Fin and Sichuan Pepper: A Sweet-Sour Memoir of Eating in China [Fuschia Dunlop] : I found this memoir to be engaging but it’s definitely got that feel of “Western person’s observations about China.” Dunlop is more aware of her situation as outsider/observer, but sometimes its hard to shake that narrative vibe. That being said, you should definitely read this if you want to know more about Chinese cuisines.

Among Others [Jo Walton] : it won a Nebula and a Hugo and I could see why. This is a really sharply observed and narrated coming-of-age story about a high school girl who is not part of the “main crowd” and finds her solace in voraciously reading all of the SciFi/Fantasy novels she can get her hands on. Really lovely writing.

The Lost Soul of Higher Education [Ellen Schrecker] : a pretty sobering read with a lot of historical background on the state of academic freedom, the corporatization of the university system, and possible ramifications for the future of the US. It was a bit depressing but well worth reading.

One Day in the Life of Ivan Denisovich [Aleksandr Solzhenitsyn] : this is Solzhenitsyn’s first book, a first-person narrative of one person’s life in a Stalinist labor camp. It really brings the grimness of the place alive — Cool Hand Luke’s prison camp had nothing on this. They’re worth comparing, I think.