Seminars Sorted by Series
Members’ Seminar
A nonabelian Brunn-Minkowski inequality
2:00pm|Simonyi Hall 101 and Remote Access
The celebrated Brunn-Minkowski inequality states that for
compact subsets $X$ and $Y$ of $\Bbb{R}^d$, $m(X+Y)^{1/d} \geq
m(X)^{1/d}+m(Y)^{1/d}$ where $m(\cdot)$ is the Lebesgue measure. We
will introduce a conjecture generalizing this inequality to...
The top-heavy conjecture for vectors and matroids
2:00pm|Simonyi Hall 101 and Remote Access
A 1948 theorem of de Bruijn and Erdős says that if $n$ points in
a projective plane do not lie all on a line, then they determine at
least n lines. More generally, Dowling and Wilson conjectured in
1974 that for any finite set of vectors spanning a...
No seminar: Presidents' Day
2:00pm|Simonyi Hall 101 and Remote Access
Astrophysical fluid dynamics
2:00pm|Simonyi Hall 101 and Remote Access
Most of the visible matter in the Universe is a plasma, that is
a dilute gas of ions, electrons, and neutral atoms. In many
circumstances, the dynamics of this plasma can be modeled in the
continuum limit, using the equations of fluid mechanics...
The Value of Errors in Proofs
2:00pm|Simonyi Hall 101 and Remote Access
A few months ago, a group of theoretical computer scientists
posted a paper on the Arxiv with the strange-looking title "MIP* =
RE", impacting and surprising not only complexity theory but also
some areas of math and physics. Specifically, it...
Higher Representation Theory
2:00pm|Simonyi Hall 101 and Remote Access
New types of symmetries have been considered in algebra and
algebraic geometry and a higher analog of representation theory has
been developed to answer questions of classical representation
theory. Geometric representation theory can be viewed as...
Estimating the mean of a real valued distribution
2:00pm|Simonyi Hall 101 and Remote Access
I revisit the basic statistical problem of estimating the mean
of a real-valued distribution. I will introduce an estimator with
the guarantee that "our estimator, on *any* distribution, is as
accurate as the sample mean is for the Gaussian...