I just heard that Benny Chor died this morning. Chor did very important work on computational biology and distributed algorithms, but I (and probably many of my readers) know him primarily for his work on cryptography, for his work on randomness extraction and for introducing the notion of private information retrieval.
I only met him once, at the event for Oded Goldreich’s 60th birthday. On the occasion, he gave a talk on the Chor-Goldreich paper, which introduced the problem of randomness extraction from independent sources, and which introduced min-entropy as the right parameter by which to quantify the randomness content of random sources. He did so using the original slides used for the FOCS 1985 talk.
I took a picture during the talk, which I posted online, and later he sent me an email asking for the original. Sadly, this was the totality of our correspondence. I heard that besides being a brilliant and generous researchers, he was a very playful, likeable and nice person. My thoughts are with his family and his friends.
This coming Fall semester the Simons Institute for the Theory of Computing in Berkeley will have in-person activities, including the really interesting program on the complexity of statistical inference, within which I will co-organize a workshop on cryptography, average-case complexity, and the complexity of statistical problems.
As it had been the case before the pandemic, all Simons Institute events will be streamed and available remotely. This includes a new series of Public Lectures called “Breakthroughs” that starts next week with a talk by Virginia Williams on matrix multiplication.
In Rome we have an expression, mai una gioia (literally, “never (a moment of) joy”) that applies well to the present times. Yesterday, there was, finally, something to be joyous about: the announcement that two of my heroes, Laszlo Lovasz and Avi Wigderson, will share the 2021 Abel Prize, one of the highest honors of mathematics.
The Bulletin of the AMS has just posted an article by Keith Ball on the legacy of Bourgain’s work on geometric functional analysis.
This beautifully written article talks about results and conjectures that are probably familiar to readers of in theory, but from the perspective of their mathematical motivations and of the bigger picture in which they fit.
I am recruiting two postdocs for two-year positions to work with me starting in Fall 2021 at Bocconi University. The positions have competitive salaries and are tax-free. If applicable, I will pay for relocation expenses, including the assistance of a relocation agency for help in finding a place to live and activate utilities, to complete immigration formalities, and to sign up for the national health care service.
Milan has been suffering as much or more than other European and American big cities for the effects of the Covid-19 pandemic. I have seen Milan in its normal condition for a few months from September 2019 to February 2020, and it is a beautiful cosmopolitan city, with an active cultural and social life, and with beautiful surroundings. Like San Francisco, it is smaller than one would expect it to be and very walkable (no hills!). Bocconi is situated in a semi-central area, about twenty minute walk from the Duomo.
I have received a large European grant that, besides paying for these postdoc positions, has a budget for senior visitors and for organizing two workshops over the duration of the grant. In particular, I was planning a workshop to be held last May in a villa on Lake Como. All such plans have been on hold, but Fall 2021 should be around the time that the global pandemic emergency ends, and I am planning for a lot of exciting scientific activity at Bocconi in the academic year 2021-22 and beyond.
I am looking for candidates with an established body of work on topics related to my research agenda, such as pseudorandomness and combinatorial constructions; spectral graph theory; worst-case and average-case analysis of semidefinite programming relaxation of combinatorial optimization problems.
In 2008, the Committee for the Advancement of Theoretical Computer Science convened a workshop to brainstorm directions and talking points for TCS
program managers at funding agencies to advocate for theory funding. The event was quite productive and successful.
A second such workshop is going to be held, online, in the third week of July. Applications to participate are due on June 15, a week from today. Organizers expect that participants will have to devote about four hours of their time to the workshop, and those who volunteer to be team leads will have a time commitment of about ten hours.
In this post we will construct a “spectral sparsifier” of a given hypergraph in a way that is similar to how Spielman and Srivastava construct spectral graph sparsifiers. We will assign a probability to each hyperedge, we will sample each hyperedge with probability , and we will weigh it by if selected. We will then bound the “spectral error” of this construction in terms of the supremum of a Gaussian process using Talagrand’s comparison inequality and finally bound the supremum of the Gaussian process (which will involve matrices) using matrix Chernoff bounds. This is joint work with Nikhil Bansal and Ola Svensson.
In the previous post we talked about Gaussian and sub-Gaussian processes and generic chaining.
In this post we talk about the Spielman-Srivastava probabilistic construction of graph sparsifiers. Their analysis requires a bound on the largest eigenvalue of a certain random matrix, that can be derived from matrix Chernoff bounds.
We will then make our life harder and we will also derive an analysis of the Spielman-Srivastava construction by casting the largest eigenvalue of that random matrix as the sup of a sub-Gaussian process, and then we will apply the machinery from the previous post.
This will be more complicated than it needs to be, but the payoff will be that, as will be shown in the next post, this more complicated proof will also apply, with some changes, to the setting of hypergraphs.
Welcome to phase two of in theory, in which we again talk about math. I spent last Fall teaching two courses and getting settled, I mostly traveled in January and February, and I have spent the last two months on my sofa catching up on TV series. Hence I will reach back to last Spring, when I learned about Talagrand’s machinery of generic chaining and majorizing measures from Nikhil Bansal, in the context of our work with Ola Svensson on graph and hypergraph sparsification. Here I would like to record what I understood about the machinery, and in a follow-up post I plan to explain the application to hypergraph sparsification.
I am recruiting for two postdoctoral positions, each for one year renewable to a second, to work with me at Bocconi University on topics related to average-case analysis of algorithms, approximation algorithms, and combinatorial constructions.
The positions have a very competitive salary and relocation benefits. Funding for travel is available.
Application information is at this link. The deadline is December 15. If you apply, please also send me an email (L.Trevisan at unibocconi.it) to let me know.