Tuesday, January 09, 2018

Double blind review at theory conferences: More thoughts.

I've had a number of discussions with people both before and after the report that Rasmus and I wrote on the double-blind experiment at ALENEX. And I think it's helpful to lay out some of my thoughts on both the purpose of double blind review as I understand it, and the logistical challenges of implementing it.

What is the purpose of double blind review?

The goal is to mitigate the effects of the unconscious, implicit biases that we all possess and that influence our decision making in imperceptible ways. It's not a perfect solution to the problem. But there is now a large body of evidence suggesting that

• All people are susceptible to implicit biases, whether it be regarding institutional status, individual status, or demographic stereotyping. And what's worse that we are incredibly bad at assessing or detecting our own biases. At this point, a claim that a community is not susceptible to bias is the one that needs evidence.
• Double blind review can mitigate this effect. Probably the most striking example of this is the case of orchestra auditions, where requiring performers to play behind a screen dramatically increased the number of women in orchestras.
What is NOT the purpose of double blind review?

Double blind review is not a way to prevent anyone from ever figuring out the author identity. So objections to blinding based on scenarios where author identity is partially or wholly revealed are not relevant. Remember, the goal is to eliminate the initial biases that come from the first impressions.

What makes DB review hard to implement at theory venues?

Theory conferences do two things that are different from other communities. We
• require that PC members do NOT submit papers
• allow PC members to initiate queries for external subreviewers.
These two issues are connected.
1. If you don't allow PC members to submit papers, you need a small PC.
2. If you have a small PC, each PC member is responsible for many papers.
3. If each PC member is responsible for many papers, they need to outsource the effort to be able to get the work done.
As we mentioned earlier, it's not possible to have PC members initiate review requests if they don't know who might be in conflict with a paper whose authors are invisible. So what do we do?

There's actually a reasonably straightforward answer to this.

• We construct the PC as usual with the usual restrictions.
• We construct a list of “reviewers”. For example, "anyone with a SODA/STOC/FOCs paper in the last 5 years” or something like that. Ideally we will solicit nominations from the PC for this purpose.
• We invite this list of people to be reviewers for SODA, and do this BEFORE paper submission
• authors will declare conflicts with reviewers and domains (and reviewers can also declare conflicts with domains and authors)
• at bidding time, the reviewers will be invited to bid on (blinded) papers. The system will automatically assign people.
• PC members will also be in charge of papers as before, and it’s their job to manage the “reviewers” or even supply their own reviews as needed.
Any remaining requests for truly external sub reviewing will be handled by the PC chairs. I expect this number will be a lot smaller.

Of course all of this is pretty standard at venues that implement double blind review.

But what if a sub-area is so small that all the potential reviewers are conflicted

well if that's the case, then it's a problem we face right now. And DB review doesn't really affect it.

What about if a paper is on the arXiv?

We ask authors and reviewers to adhere to double blind review policies in good faith. Reviewers are not expected to go hunting for the author names, and authors are expected to not draw attention to information that could lead to a reveal. Like with any system, we trust people to do the right thing, and that generally works.

But labeling CoI for so many people is overwhelming.

It does take a little time, but less time than one expects. Practically, many CoIs are handled by institutional domain matching, and most of the rest are handled by explicit listing of collaborators and looking for them in a list. Most reviewing systems allow for this to be automated.

But how am I supposed to know if the proof is correct if I don't know who the authors are.

Most theory conferences are now comfortable with asking for full proofs. And if the authors don't provide full proofs, and I need to know the authors to determine if the result is believable, isn't that the very definition of bias?

And finally, from the business meeting....

Cliff Stein did an excellent job running the discussion on this topic, and I want to thank him for facilitating what could have been, but wasn't, a very fraught discussion. He's treading carefully, but forward, and that's great. I was also quite happy to see that in the straw poll, there was significant willingness for trying double blind review (more than the ones opposed). There were still way more abstentions, so I think the community is still thinking through what this might mean.

Sunday, January 07, 2018

Report on double blind reviewing in ALENEX 2018

+Rasmus Pagh and I chaired ALENEX 2018, and we decided to experiment with double blind review  for the conference. What follows is a report that we wrote on our experiences doing this. There are some useful notes about logistics, especially in the context of a theoretically-organized conference on experimental algorithms.

ALENEX 2018 Double Blind Review

For ALENEX 2018, we decided to experiment with a double blind review process i.e one in which authors and reviewers were unaware of each others’ identity. While double blind review is now almost standard in most computer science conferences, it is still relatively uncommon in conferences that focus on theoretical computer science and related topics.

The motivation

In the original argument we presented to the ALENEX Steering Committee, we presented the following reasons for why we wanted double blind review:
1. Eliminating bias.
Andrew Tomkins did an experiment for WSDM this year and wrote a report on it: https://arxiv.org/abs/1702.00502

One particular observation:

"Reviewers in the single-blind condition typically bid for 22% fewer papers, and preferentially bid for papers from top institutions. Once papers were allocated to reviewers, single-blind reviewers were significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors and top institutions. The estimated odds multipliers are 1.66 for famous authors and 1.61 and 2.10 for top universities and companies respectively, so the result is tangible”

2. Common practice.

Virtually every CS community except theory is doing double blind review, including most of ML (NIPS, ICML), DB (VLDB, SIGMOD), Systems (NSDI), etc. While theory papers have their own idiosyncrasies, we argued that ALENEX is much closer in spirit and paper structure to more experimental venues like the ones listed.

3. Limited burden on authors and reviewers for an experiment

There was no real logistical burden. We were not blocking people from posting on the arXiv, or talking about their work. We’re merely requiring submissions be blinded (which is easy to do). For reviewers also, this is not a problem - typically you merely declare conflicts based on domains and that takes care of the problem of figuring out who’s conflicted with what paper (but more on this later).

4. Prototyping.

While theoryCS conferences in general do not make use of double blind review, ALENEX is a small but core venue where such an experiment might reveal useful insights about the viability of double blind overall. So we don’t have to advocate changes at SODA/STOC/FOCS straight up without first learning how it might work.

5. PC submissions.

We are allowing PC members to submit papers, and this has been done before at ALENEX. In this case double blind review is important to prevent even the appearance of conflict.

The process

Before submission: We provided a submission template for authors that suppressed author names. We also instructed authors on how to refer to prior work or other citations that might leak author identity - in brief, they were asked to treat these as any third-party reference. We also asked authors to declare conflicts with PC members.
After submission/before reviews: We recognized that authors might not be used to submitting articles in double blind mode and so went over each submission after it was submitted and before we opened up bidding to PC members to make sure that the submissions were properly blinded. In a few cases (less than 10/49) we had to ask authors to make modifications.
During review process: The next step was to handle requests for subreviewers. Since PC members could not determine CoIs (conflicts of interest) on their own, all such requests were processed through the PC chairs. A PC member would give us a list of names and we would pick one. (so more private information retrieval than a zero knowledge protocol!)

Issues

A number of issues came up that appear to be unique to the theory conference review process. We document them here along with suggested mitigations.
1. Managing the CoI process: In theoryCS conferences, subreviewing happens outside the umbrella of the PC. PC members have the power to request any number of subreviewers for papers, and this process happens after the papers are submitted. In contrast, in other venues, subreviewers essentially function as members of the PC - they are invited to be reviewers ahead of time, and are listed when the author declare conflicts of interest. This means that under the process we used, PC members cannot determine for themselves whether a subreviewer has a CoI with a paper, whereas in the alternate process, this is taken care of automatically. One possible mitigation is to ask PC members to list potential reviewers ahead of time and have them registered in the system for authors to declare CoI with. While this might generate a long list of subreviewers for authors to review, this process is customarily handled by a) allowing authors to declare conflicts by affiliation (domain name) and then b) presenting them with a filtered set of reviewers to mark conflicts with. Domain-based filtering is probably the most effective method for handling conflicts based on current or recent affiliation: it allows for reviewers to be added after the fact, and systems like Microsoft’s CMT allow for it.
2. The difficulty of hiding identity based on prior work: In experimental work, a group will often write a series of papers that builds on infrastructure that they’ve developed. The relative difficulty of building such infrastructure also means that groups become “known” for working in certain areas. This made it a little difficult for authors to honestly blind their identity, because their papers clearly built on software that wasn’t publicly available and therefore had to be part of their group. The solution of blinding that reference itself does not always work because then it is hard to evaluate the quality of the work described in the paper. We note that this problem occurs in other, more experimental parts of CS. The typical solution is to continue with the blinding effort in any case, and make an effort to release code publicly, so anyone could have used the code being built on. In our view, this is a less significant problem than the first point. To this end, here are some guidelines from CHI and CSCW (both ACM conferences).
3. Is the paper provably double blind? A common complaint about double blind review is that it is not perfect -- that it’s possible with some effort to determine some subset of the authors with some degree of certainty. The response that we gave when asked, and that is usually given, is that the goal of double blind review is not to provide a zero knowledge protocol, but to prevent the immediate implicit bias that comes from directly seeing the author names prior to reading the paper. We note that this is a common complaint from people in the theory community: however our experience with double blind review in other venues has been that after a while, one gets accustomed to reviewing papers without attempting to first determine the authors and the process works as intended.

Feedback

We also solicited feedback from the program committee after the review process was complete. We asked them three questions:
1. What did you like (and what worked) about the double blind review process instituted this year for ALENEX?
2. What in your opinion did NOT work?
3. Is there any other feedback you'd like to provide about the double blind review process?
The responses we got for question 1 were uniformly of the form of “I’m glad that we did it, and felt that papers got a fairer shake than they would have otherwise”. One PC member even said unequivocally that the double blind review process made it more likely that they would submit to ALENEX in the future.
On question 2, PC members brought up the issues we raised above, recommending that we make it clearer to authors how they need to blind their submissions and also mentioning the difficulty of assigning subreviewers.
On question 3, the feedback was uniformly positive in favor of continuing with double blind review, inspite of the issues raised.

Summary

On balance, our experience with double blind review was positive. While there were logistical issues, many of these can be resolved by the methods we describe above. Further, there is now a wealth of knowledge accumulated in other areas of computer science that we can learn from. SIGPLAN has built a comprehensive FAQ around this issue that answers many of the questions that arise.
We recommend continuing with double blind review for at least the next two years at ALENEX, firstly because this brings us in line with common practice in most other parts of computer science, and secondly because many of the logistical issues people face with DB review will go away with habituation. At that point, the potential inconvenience of the process reduces and will be outweighed by the benefits in transparency and lack of bias.

Sunday, October 22, 2017

Cake cutting algorithms in prison

This past Friday, I gave a lecture on cake cutting algorithms at the Timpanogos Women's Facility as part of a lecture series organized by my Utah colleague Erin L. Castro and her Utah Prison Education Project. The project's mission is to
... provide quality, sustained, and meaningful higher educational opportunities to individuals incarcerated in Utah state prisons. Through embodying the mission of the University of Utah, the Project assists incarcerated students and non-incarcerated volunteers to live lives of impact, both in prison and post-incarceration, by fostering leadership, civic engagement, and critical inquiry. UPEP aims to create lasting impact in our state and our communities by investing in people and providing them the tools necessary for empowerment and lifelong learning.
I think this is incredibly important work.  We don't need to get into a much larger discussion about rehabilitation versus punishment theories of justice to appreciate how providing access to education might allow incarcerated students the ability to turn their life around, or even find opportunities for work once they leave prison so that they have a way to support themselves without falling back into criminal activities. Maybe the amount of education they get in prison might even one day be a predictive factor in deciding whether they will reoffend!

When I first mentioned this on Facebook, many people were curious about what it was like. I can report here that my lecture was.... more or less exactly like a lecture would happen in any of the other places I lecture. Students came in with a lot of fear of math (which is why I thought I'd talk about recreational math). There was an actual cake and lots of nervousness when I had students run the algorithms on the cake. There were lots of questions about the different models of fair division, and some confusion about whether we could trust the results of the process.

In other words, every kind of question one might expect in any setting. The students were engaged and interested. They hadn't had too much math experience except what they did in high school, but they were able to follow along quite well and come up with their own algorithms as we advanced further into the lecture. I enjoyed myself, and I hope they did too!

But of course this was at a prison, so there were some other details.

• Arranging the cake (and a whiteboard) took some work, and Erin and her group (as well as the lieutenant at the prison) did their magic to make it happen. There was some discussion about who would use the plastic butter knife: eventually the students did.
• I couldn't bring my laptop in, and used a whiteboard (which worked fine for my talk). But I hadn't planned on using one, and maybe if I did want to do a slide presentation that would have been arranged as well.
• There was some discussion about where the students would sit, and how much spacing was needed. There was even some minor discussion about whether we could all get together for a group photo at the end (we did!).

Friday, September 29, 2017

On music, mathematics and teaching.

I'm a perpetual student when it comes to my guitar-playing. I started off learning acoustic guitar, and taught myself a little bass in college. When I was in the college band our music advisor played some classical guitar and that got me hooked.

I've had a number of teachers through grad school and beyond, but I've always plateaued out at a level where I'm competent but no better. At some point I realized that what motivated me to play was the right kinds of music (this I also learnt when watching my children learn an instrument), and that inexorably led me to my new quest: learning flamenco guitar.

Flamenco is a very passionate style of playing - and classical guitar can seem bloodless and sedate in comparison. It also requires many different right hand techniques that are not common in classical guitar problem.

The net result is that I'm back to being a beginning student again - struggling with mechanics, hand position and note playing. It's a lot of frustration with the occasional moment of transcendence. I whine at my teacher in the way students whine at me, and he's sneaky enough that now he just asks me "so what would you tell your own students" and that shuts me up.

Which brings me to the point of this post (what??? posts need a point?). We spent a lesson last week talking about extracting expression and feeling from the instrument. I kept asking him about what tools I could use (beyond the usual tone control by moving up and down the fretboard and using volume) to express more emotion, and what emotion that would be. His response was first to show me this beautiful video of an interviewer "talking" to Paco De Lucia's guitar

and then explain to me that I have to dig deep within myself to find the way I can relate to the music.

And then it hit me (painfully). Aditya Bhaskara and I are running a theory seminar on reading classic theory papers where (much like my previous seminar) there's a strong emphasis on getting to the core ideas and intuitions that drive a result. I'm constantly exhorting students (even more so than Aditya - I think it's interesting to see how different people absorb messages from a paper) to find the core intuition in the paper and be able to express it in a short "story" almost.

And that's essentially what my teacher is exhorting me to do. In both cases, the expert is trying to get the student to transcend the purely mechanical aspects of (reading the paper/playing the instrument) and get to the deeper (mathematical/emotional) truth of the (paper/piece). And it's hard precisely because the student in either case is still struggling with the mechanical, and doesn't yet have the facility with the tools to let them fall away.

Does this mean I'll be a more enlightened teacher? I doubt it :). But I do have a little more sympathy for my students.

"X is a social construct" and the perils of mining behavior.

After the infamous Google memo (and frankly for much longer if you work in algorithmic fairness), the idea of something being a "social construct" has popped up again, and I will admit that I've struggled with trying to understand what that means (damn you, focused engineering education!)

Ta-Nehisi Coates' article about race is a short and excellent read. But I also want to highlight something much closer to home. BYU Radio's Julie Rose did an interview with Jacqueline Chen (at the U) on her recent work on perceptions of race in the US vs Brazil.

The interview is here (and it's short - starting at around 20 minutes in) and in it Prof. Chen very masterfully lays out the way in which race is perceived and how it changes based on changes in context. The interview is based on a recently published paper ($$). One important takeaway: the way in which one's racial identity is perceived varies greatly between the US (which appears to be influenced by parental information) vs Brazil (where skin color appears to be the dominant factor). More importantly, the idea of race as immutable vs changeable, a categorical attribute versus a continuous one, all vary. And that's what we mean by saying that X (here, race) is a social construct. It's not saying that it's fictitious or less tangible. But that it's defined by the way we talk about it in society. Why is this important? When we collect data as a way to predict behavior, we're making an implicit claim that behavior can be predicted (and explained) by intrinsic and often immutable descriptors of an individual. We use (or don't use) "race" as a feature when building models. But this itself is a huge assumption! It assumes that we can intelligently ascribe features to individuals that capture these notions, and that they are defined solely by the individual and not by context. The brilliant Medium article about the paper that claimed to predict criminality from facial features makes this point very well. But do we capture the entire history of environmental factors that make up the story of an individual. Of course not. We essentialize an individual into a collection of features that we decide captures all their relevant traits for the purpose of prediction, and then we build a model that rests on this extremely problematic idea. Much of the work I do on fairness can be reduced to "check your data, and check your algorithm". What we're also thinking about (and that directly speaks to this issue) is "check your features". It turns out that way back in 1921, Walter Lippman had something interesting to say about all of this. From a longer essay that he wrote on the importance of frames as mediating how we perceive the world (and it says something about fake news and "true facts" as well): And so before we involve ourselves in the jungle of obscurities about the innate differences of men, we shall do well to fix our attention upon the extraordinary differences in what men know of the world. I do not doubt that there are important biological differences. Since man is an animal it would be strange if there were not. But as rational beings it is worse than shallow to generalize at all about comparative behavior until there is a measurable similarity between the environments to which behavior is a response. Wednesday, August 23, 2017 On free speech, gerrymandering and self-contradictory rule systems In the light of the wave of racist and neo-Nazi bile being slung around in Charlottesville and beyond, Karl Popper's Paradox of Tolerance has been doing the rounds. Paraphrased, it can be phrased as In a tolerant society, one must be tolerant of everything except intolerance. There's an interesting self-referential element there that's reminiscient of Gödel's incompleteness theorems. To wit, We can either have a system of rules that is consistent, but cannot account for all phenomena that are consistent with the rules, or have a system that is inconsistent. In other words, the principle of tolerance in society cannot incorporate its own negation and still survive. One nuance here is that Popper doesn't necessarily advocate for restricting intolerant speech as much as speech that leads to incitement, which is somewhat in line with 1st Amendment protections in the US. This reminds me of another situation where self-contradictory rule systems cause problems: gerrymandering. The Supreme Court is soon to hear arguments in a case of partisan gerrymandering from Wisconsin. Roughly speaking, a partisan gerrymander (as opposed to a racial gerrymander) is one in which districts are drawn to favor a certain political party (the "partisan" aspect") as opposed to favoring a certain race. While racial gerrymanders have been repeatedly struck down in court (the latest being a case from Texas), partisan gerrymanders have a much more mixed record, which is why many are watching this new case with great interest. One potential argument in favor of allowing partisan gerrymandering is that if a party wins, their victory should allow them the power to redraw districts -- the "elections have consequences" principle. But it seems to me that this is again an example of Popper's paradox of tolerance. That is to say, if we allow the party that wins an election to do partisan gerrymandering, then we are allowing through the democratic process an action that will serve to directly reduce the ability of the democractic process to represent the people. And for that reason a partisan gerrymander should not be allowed. I wonder if there are other settings where this principle might help clarify the limits of a permissive rule structure. Friday, June 23, 2017 TheoryFest Day 3: Plenaries I was the chair of the plenary session on Wednesday, so was too focused on keeping track of time and such to pay full attention to the talks. Having said that, all the speakers we've had so far have done a bang-up job of keeping within their time window without much prompting at all. So I can only give my very brief thoughts on the talks. For more information, go here. Atri Rudra was up first with a neat way to generalize joins, inference in probabilistic models and even matrix multiplication all within a generic semi-ring framework, which allowed the authors to provide faster algorithms for join estimation and inference. In fact, these are being used right now to get SOTA join implementations that beat what Oracle et al have to offer. Neat! Vasilis Syrgkakis asked a very natural question: when players are playing a game and learning, what happens if we treat all players as learning agents, rather than analyzing each player's behavior with respect to an adversary? It turns out that you can show better bounds on convergence to equilibrium as well as approximations to optimal welfare (i.e the price of anarchy). There's more work to do here with more general learning frameworks (beyond bandits, for example). Chris Umans talked about how the resolution of the cap set conjecture implies bad news for all current attempts to prove that \omega = 2 for matrix multiplication. He also provided the "book proof" for the cap set conjecture that came out of the recent flurry of work by Croot, Lev, Pach, Ellenberg, Gijswijt and Tao (and cited Tao's blog post as well as the papers, which I thought was neat). I hope the slides will be up soon. If not for anything else, for Atri's explanation of graphical models in terms of "why is my child crying", which so many of us can relate to. Minority Report and Predictive Policing Minority report (the movie) is 15 years old. Who knew! Well I certainly didn't, till I was called by a reporter from CNN who wanted to talk about the legacy of the movie. Here's the link to the story. It was a depressing conversation. We went over some of the main themes from the movie, and I realized to my horrow how many of them are now part of our reality. Precogs are now PredPol. Algorithms that claim to know where crime will happen. The companies building predictive policing software will often take umbrage at references to Minority Report because they say they're not targeting people. But all I say is "….yet". Predictions have errors. The very title of the movie telegraphs the idea of errors in the prediction system. And much of the movie is about a coverup of such a 'minority report'. And yet today we treat our algorithms (precogs) as infallible, and their predictions as truth. VERY personalized advertising. The main character is targeted by personalized advertising and a good section of the plot involves him trying to get a replacement eyeball so retina scans don't detect him. And then we have this. Feedback loops. The debate between Agatha (the minority precog) and Anderton about free will leads him to a decision to change his future, which then changes the prediction system. In other words, feedback loops! But feedback loops work both ways. Firstly, predictions are not set in stone: they can be changed by our actions. Secondly, if we don't realize that predictions can be affected by feedback from earlier decisions, our decision-making apparatus can spiral out of control, provably so (consider this a teaser: I'll have more to say in a few days). What's a little sad for me is because I wasn't sufficiently 'woke' when I first saw the movie, I thought that the coolest part of it was the ingenious visual interfaces on display. We're actually not too far from such systems with VR and AR. But that now seems like such a minor and insignificant part of the future the movie describes. TheoryFest Day 3: Streaming symmetric norms. There's a weird phenomenon in the world of streaming norm estimation: For \ell_0, \ell_1, \ell_2 norm estimation, there are polylog (or less)-space streaming approximation algorithms. But once you get to \ell_p, p \ge 3, the required space suddenly jumps to polynomial in n. What's worse is that if you change norms you need a new algorithm and have to prove all your results all over again. This paper gives a universal algorithm for estimating a class of norms called "symmetric' (which basically means that the norm is invariant under coordinate permutation and sign flips - you can think of this as being invariant under a very special class of rotations and reflections if you like). This class includes the \ell_p norms as a special case, so this result generalizes (upto polylog factors) all the prior work. The result works in a very neat way. The key idea is to define a notion of concentration relative to a Euclidean ball. Specifically, Fix the unit \ell_2 ball in n dimensions, and look at the maximum value of your norm \ell over this call: call it b_\ell. Now look at the median value of your norm (with respect to a uniform distribution over the sphere): call it m(\ell). Define the modulus of concentration as$$ mc(\ell) = \frac{b_\ell}{m_\ell} 
Notice that this is 1 for $\ell_2$. For $\ell_1$, the maximum value is larger: it's $\sqrt{d}$. The median value as it turns out is also $\Theta(\sqrt{d})$, and so the modulus of concentration is constant. Interestingly, for $p > 2$, the modulus of concentration for $\ell_p$ is $d^{1/2(1 - 2/p)}$ which looks an awful lot like the bound of $d^{1-2/p}$ for sketching $\ell_p$.

As it turns out, this is precisely the point. The authors show that the streaming complexity of any norm $\ell$ can be expressed in terms of the square of $mc(\ell)$. There are some technical details - this is not exactly the theorem statement - but you can read the paper for more information.

Update: Symmetric norms show up in a later paper as well. Andoni, Nikolov, Razenshteyn and Waingarten show how to do approximate near neighbors with $\log \log n$ approximation in spaces endowed with a symmetric norm. It doesn't appear that they use the same ideas from this paper though.

Thursday, June 22, 2017

TheoryFest Day 3: "I forgot to talk about Kant"

The above is an actual quote from Oded Goldreich in his hilarious speech accepting the Knuth Prize for 2017. This speech was highly anticipated, because most of us have spent years reading and marvelling at Oded's opinions (he addressed the elephant in the room very promptly)

As the title suggests, there was a heavy dose of philosophy, with quotes from Kant, Weber, Frankl, and MacIntyre. He also gave a brief technical exposition of new developments in interactive proofs starting from the "IP for Muggles" paper of Goldwasser, Kalai and Rothblum. This topic is quite dear to me right now, given that my student Samira Daruki just defended a thesis that is at least partly about interactive proofs where the verifier is extremely weak (aka streaming). I'll have more to say about this later.

One can only hope the video of Oded's talk will be available online soon: it's can't miss-TV :).

(I'm keeping these posts short in the hope that I'll actually write them. The danger in longer posts is that they never get written).

TheoryFest Day 3: Panels and Lunches

(for various reasons, I don't have wifi access at my Airbnb, so my posts are delayed. But it's not like you're hanging on my every word...... are you?)

Day 3 started off with a panel moderated by Anna Karlin, starring Cynthia Dwork, Russell Impagliazzo, Ankur Moitra, Tim Roughgarden, Dan Spielman and Andy Yao. I personally like panels to have a bit of an edge and controversy, and there wasn't that much of it here, but there was some good discussion about various aspects of TCS, as well as some good advice for the young'uns in the audience. Some of the highlights:

• Andy Yao thinks a working quantum computing device is imminent, but one that we could solve Rubik's cube puzzles on might take a while longer.
• Cynthia Dwork made a strong argument for the power of theoryCS to define problems clearly and rigorously even for messy concepts like privacy and fairness.
• Ankur Moitra made a point that I think should be made more often: that modeling and algorithm design are fundamentally different things even if they might be influenced by one another. And it's important not to confuse the two.
• Dan Spielman tried to emphasize that even if it might seem like senior people at a conference know what they're talking about, they don't really know. And that's it's ok to be in a constant state of befuddlement. As Anna Karlin put it I think, "We must learn to dance with chaos".
There was lots more that the panel discussed, but these are what stuck in my mind. One pity was that we ran out of time and couldn't have any audience questions.

As I had mentioned earlier, there were networking lunches for senior (aaaah, I'm a senior now :() and junior people. Never one to let of a chance to pontificate, I had lunch with Sima Hajiaghaei (UVic), Lalla Mouatadid (Toronto), Benjamin Priest (Dartmouth) (ed: you need a website!) and Shravas Rao (NYU) at a nice dumpling place in Chinatown. They won't like me saying this, but I will anyway :) - they all seemed so young and enthusiastic I felt a little old. But we had a great chat, and if you bump into any of them at a theory conference in a future make sure to say hi :).