It seems like I’ve been waiting forever to make this post! Back in the fall, I helped to organize an Alumni College at Caltech centered around the theme of “CS+X”. Now, I’m very excited to announce that the videos from the event are up!
What is an alumni college you ask? Well, instead a homecoming game or something like that, we get alumni back to Caltech by promising a day of research talks, well really thinks like TED talks! So, Alumni College focuses on a different theme each year, and then does a day of provocative talks on that topic. This year the theme was “Disrupting Science and Engineering with computational thinking” i.e., the disruptive power of CS+X.
As I’ve written about before, we view “CS+X” as what makes Caltech’s approach to computer science so distinctive compared to other schools. We pride ourselves on inventing fields and then leaving them for others once they’re popular so that we can invent the next field. So, “seeding fields and then ceding them”…
In any case, the alumni college was a day filled with talks on CS+X from researchers at Caltech that are leading new fields… We covered CS+Astronomy, CS+Physics, CS+Biology, CS+Economics, CS+Chemistry, CS+Energy, and so on…
You can watch all of them on Youtube here. Enjoy!
As I was flying to the NSDI PC meeting this week I was catching up on reading and came across an article on privacy in the Atlantic that (to my surprise) pushed nearly the same perspective on privacy that we studied in a paper a year or so ago… Privacy as plausable deniability.
The idea is that hacks, breaches, monitoring behavior, etc. are so common and hard to avoid that relying on tools from crypto or differential privacy isn’t really enough. Instead, if someone really cares about privacy they probably need to take that into account in their actions. For example, you can assume that google/facebook/etc. are observing your behavior online and that this is impacting prices, advertisements, etc. Tools from privacy, encryption, etc. can’t really help with this. However, tools that add “fake” traffic can. If an observer knows that you are using such a tool then you always have plausible deniability about any observed behavior, and if these are chosen carefully, then they can counter the impact of personalized ads, pricing, etc. There are now companies such as “Plausible Deniability LLC” that do exactly this!
On the research front, we looked at this in the context of the following question: If a consumer knows that their behavior is being observed and cares about privacy, can the observer infer the true preferences of the consumer? Our work gives a resounding “no”. Using tools from revealed preference theory, we show that the observer not only cannot learn, but that every set of observed choices can be “explained” as consistent with any underlying utility function from the consumer. Thus, the consumer can always maintain plausible deniability.
If you want to see the details, check it out here! And, note that the lead author (Rachel Cummings) is on the job market this year!
P.S. The NSDI PC meeting was really stimulating! It’s been a while since I had the pleasure of being on a “pure systems” PC, and it was great to see quite a few rigorous/mathematical papers be discussed and valued. Also, it was quite impressive to see how fair and thorough the discussions were. Congrats to Aditya and Jon on running a great meeting!
Over the last year, while I haven’t been blogging, one of the new directions that we’ve started to look at in RSRG is “data markets”.
“Data Markets” is one of those phrases that means lots of different things to lots of different people. At its simplest, the idea is that data is a commodity these days — data is bought and sold constantly. The challenge is that we don’t actually understand too much about data as an economic good. In fact, it’s a very strange economic good and traditional economic theory doesn’t apply…
Every year (since 2009) in the fall, all of the folks in southern California that work at the border of economics and CS/EE get together for the Network Economics and Game Theory (NEGT) workshop. The hosting duties rotate between USC, UCLA, and Caltech, and this year the honor falls to us here at Caltech.
We’ve just finished finalizing the program — and it’s a great one. So, if you’re in the area, come on by!
We’re holding it on Nov 20-21. We’ll have a very reasonable start time each day of 10am so that folks try to avoid LA traffic in the morning, and we’ll end both days with a reception so that you can avoid traffic on the way home, too. Markus Mobius (MSR) and Tim Roughgarden (Stanford) are the keynotes, and then we have a great list of invited speakers from all across Southern California to round out the program.
Attendance is free, but please register early, if possible, so that we can plan the catering! Also, we’ll have a poster session for students to present work (and work-in-progress). If you’re interested, just sign up when you register.
June is a month that is dominated by conference travel for me, with three of my favorite conferences all typically happening back-to-back. The third (and final) of these this year was Stochastic Networks. The little one at home prevented me from being able to join for the whole conference, but I was happy to be able to come for the first two days.
Stochastic Networks is an applied probability conference that is the type of event that doesn’t happen often enough in computer science. Basically, it consists of 20-25 invited hour-long talks over a week. The talks are mostly senior folks with a few junior folks thrown in, and are of an extremely high quality. And, if you do the math, that makes an average of 4-5 talks per day, which means that the days leave a lot of time for conversation and interaction. Because of the quality of the speakers, there are still lots of folks that attend even if they aren’t presenting (which makes for somewhere around a 100+ person audience, I’d guess), so it becomes a very productive event, both in terms of working with current collaborators and in terms of starting up new projects.
Well, June is conferences season for me, so despite a new baby at home I went off on another trip this week — sorry honey! This time it was ACM Sigmetrics in Austin, where I helped to organize the GreenMetrics workshop, and then presented one of our group’s three papers on the first day of the main conference.
This past week, a large part of our group attended ACM EC up in Palo Alto. EC is the top Algorithmic Game Theory conference, and has been getting stronger and stronger each year. I was on the PC this year, and I definitely saw very strong papers not making the cut (to my dismay)… In fact, one of the big discussions at the business meeting of the conference was how to handle the growth of the community.
Finding about about the increasingly difficult acceptance standards, I was even happier that our group was so well-represented. We had four papers on a variety of topics, from privacy to scheduling to equilibrium computation. I’ll give them a little plug here before talking about some of my highlights from the conference…