Current Seminars
Upcoming Seminars

Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
MiniCourse: Introduction to Fluctuations of BetaEnsembles
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Gaultier Lambert (Universität Zürich)To participate in this seminar, please register HERE.
We provide an introduction to recent results on the large N behavior of betaensembles, also known as loggases. In the first part, we focus on the rigidity property of the spectrum which provides fine estimates on the fluctuations of eigenvalues and explain how this relate to universality. In the second part, we explain how to prove the CLT for linear statistics using loop equations and mention the connection to logcorrelated fields and Gaussian multiplicative chaos.
Updated on Oct 22, 2021 08:17 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Positivity and Universality (from a Combinatorial Perspective)
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Natasha Blitvic (University of Lancaster)To participate in this seminar, please register HERE.
Many classical combinatorial sequences are moments of positive Borel measures on the real line. Furthermore, several universal laws in probability correspond in this manner to sequences that are equally ubiquitous in combinatorics. Starting from these two observations, we explore the boundary between probability and combinatorics. We introduce a unifying combinatorial framework that brings together (and interpolates between) structures that are significant in both fields, with particular focus on permutations and set partitions. This approach gives insight into a hard open problem in combinatorics, while providing a new perspective on several classical and noncommutative limit theorems and on moments of classical orthogonal polynomials and their qanalogues. Based on joint work with Einar Steingrímsson.
Updated on Oct 21, 2021 09:24 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:21 PM PDT 
Fellowship of the Ring: Support Theories for NonCommutative Complete Intersections
Location: MSRI: Online/Virtual Speakers: Julia Pevtsova (University of Washington)To attend this seminar, you must register in advance, by clicking HERE.
For a finite dimensional Hopf algebra A over a field k the cohomological support for the singularity category Sing A can be defined via the action of the cohomology algebra $H^*(A,k)$ with little reference to the tensor structure. Yet, for various finite tensor categories the cohomological support turns out to respect that structure via the “tensor product property”: $supp(M \otimes N) = supp M \cap supp N$. When the property holds, it often appears to be intimately connected with some kind of alternative description of the cohomological support, “a rank variety”. I’ll describe such an alternative construction, {\it the hypersurface support}, which goes back to the work of Eisenbud, Avramov, Buchweitz and Iyengar in commutative algebra, for the Hopf algebras which are ``noncommutative complete intersections”. One application of this construction is to the open question of “whether tensor product property holds for small quantum groups”, another to calculations of the Balmer spectrum. Joint work with Cris Negron.
Updated on Oct 20, 2021 11:40 AM PDT 
MiniCourse: The Quest for Fredholm Determinants
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Harini Desiraju (University of Birmingham)To participate in this seminar, please register HERE.
In this course I will present two techniques to construct Fredholm determinants starting from an integrable system. One of these techniques will be based on the RiemannHilbert method and the other only requires the knowledge of the associated linear system. My choice of examples will be Painlev\'e equations, although the techniques are applicable to a wide variety of problems.
Updated on Oct 19, 2021 02:58 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
MiniCourse: Introduction to Fluctuations of BetaEnsembles
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Gaultier Lambert (Universität Zürich)To participate in this seminar, please register HERE.
We provide an introduction to recent results on the large N behavior of betaensembles, also known as loggases. In the first part, we focus on the rigidity property of the spectrum which provides fine estimates on the fluctuations of eigenvalues and explain how this relate to universality. In the second part, we explain how to prove the CLT for linear statistics using loop equations and mention the connection to logcorrelated fields and Gaussian multiplicative chaos.
Updated on Oct 22, 2021 08:18 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:22 PM PDT 
MiniCourse: The Quest for Fredholm Determinants
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Harini Desiraju (University of Birmingham)To participate in this seminar, please register HERE.
In this course I will present two techniques to construct Fredholm determinants starting from an integrable system. One of these techniques will be based on the RiemannHilbert method and the other only requires the knowledge of the associated linear system. My choice of examples will be Painlev\'e equations, although the techniques are applicable to a wide variety of problems.
Updated on Oct 19, 2021 03:28 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Longest Increasing Subsequence and the Schensted Shape of Some PseudoRandom Sequences
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Karl Liechty (DePaul University)To participate in this seminar, please register HERE.
For uniformly random permutations of length n, it is well known that the length of the longest increasing subsequence is very close to 2 \sqrt{n}. More generally, the Schensted shape of the permutation (under Schensted insertion) rescaled by 1/\sqrt{n} converges to a certain nonrandom limit shape and described by VershikKerov and LoganShepp. When looking at a sequence of numbers which claims to be "pseudorandom"
, one could ask whether the longest increasing subsequence and the Schensted shape have similar limits. For most pseudorandom sequences, I do not know the answer to this question so there will be some open questions posed. For the sequence consisting of the fractional parts of multiples of an irrational number, the answer is "no", and I will discuss joint work with T. Kyle Petersen which explores the behavior of the Schensted shape, which can be described explicitly in terms arithmetic properties of the irrational number which generates the sequence. Updated on Oct 20, 2021 02:30 PM PDT 
Program Associates' Seminar: an Introduction to Duality
Location: MSRI: Simons Auditorium, Online/Virtual Speakers: Chiara Franceschini (Instituto Superior Técnico)To participate in this seminar, please register HERE.
Updated on Oct 22, 2021 03:17 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:22 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
The Analysis and Geometry of Random Spaces  Virtual Participant
Location: MSRI: Online/VirtualUpdated on Apr 07, 2021 10:48 AM PDT 
Complex Dynamics: from special families to natural generalizations in one and several variables  Virtual Participant
Location: MSRI: Online/VirtualUpdated on Apr 07, 2021 10:49 AM PDT

2022 African Diaspora Joint Mathematics Workshop
The African Diaspora Joint Mathematics Workshop (ADJOINT) is a yearlong program that provides opportunities for U.S. mathematicians – especially those from the African Diaspora – to form collaborations with distinguished AfricanAmerican research leaders on topics at the forefront of mathematical and statistical research.
Beginning with an intensive twoweek summer session at MSRI, participants work in small groups under the guidance of some of the nation’s foremost mathematicians and statisticians to expand their research portfolios into new areas. Throughout the following academic year, the program provides conference and travel support to increase opportunities for collaboration, maximize researcher visibility, and engender a sense of community among participants. The 2022 program takes place June 20  July 1, 2022 in Berkeley, California.
Updated on Oct 13, 2021 03:27 PM PDT
Past Seminars

Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Welcome Tea
Updated on Aug 25, 2021 11:32 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Program Associates' Seminar
Updated on Oct 04, 2021 10:56 AM PDT 
Seminar Avoiding Local Parametrix Problems in RiemannHilbert Theory
Updated on Oct 08, 2021 12:40 PM PDT 
Seminar Spectral Theory of NonSelfAdjoint Dirac Operators on the Circle
Updated on Oct 08, 2021 11:06 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar MiniCourse: Correlation Functions of the SinhGordon Quantum Field Theory in 1+1 Dimensions Part II
Updated on Oct 13, 2021 10:10 AM PDT 
Seminar Random Matrices and Random Landscapes
Updated on Sep 03, 2021 12:17 PM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Independence Preserving Transformations and Exact Solvability
Updated on Oct 08, 2021 10:52 AM PDT 
Seminar Colloquium: Topological Expansion and Phase Diagram for Ensembles of Random Matrices with Complex Potentials
Updated on Oct 08, 2021 10:27 AM PDT 
Seminar Integrable Probability Open Problem Session
Updated on Oct 04, 2021 10:49 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar MiniCourse: Correlation Functions of the SinhGordon Quantum Field Theory in 1+1 Dimensions Part I
Updated on Oct 13, 2021 10:12 AM PDT 
Seminar Fellowship of the Ring: The FiberFull Scheme
Updated on Oct 11, 2021 01:11 PM PDT 
Seminar Random Matrices and Random Landscapes
Updated on Sep 03, 2021 12:15 PM PDT 
Seminar Colloquium: A Survey of Results for Asymptotics of Determinants of Operators
Updated on Oct 08, 2021 11:35 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Program Associate Short Talks (3x 25 mins)
Updated on Oct 04, 2021 10:45 AM PDT 
Seminar Welcome Tea
Updated on Aug 25, 2021 11:32 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Program Associates' Seminar: probabilistic conformal blocks
Updated on Sep 30, 2021 08:31 AM PDT 
Seminar Deformations of Toeplitz Determinants: Applications, Asymptotics, and Orthogonality Structures
Updated on Sep 27, 2021 04:08 PM PDT 
Seminar Spherical Integrals and Large Deviations of the Largest Eigenvalues for SubGaussian Random Matrices
Updated on Sep 28, 2021 08:42 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT