# Mathematical Sciences Research Institute

Home > Scientific > Colloquia & Seminars > All Colloquia & Seminars > Upcoming

1. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
2. # Mini-Course: Introduction to Fluctuations of Beta-Ensembles

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 beta-ensembles, also known as log-gases. 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 log-correlated fields and Gaussian multiplicative chaos.

Updated on Oct 22, 2021 08:17 AM PDT
3. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
4. # 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 q-analogues. Based on joint work with Einar Steingrímsson.

Updated on Oct 21, 2021 09:24 AM PDT
5. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:21 PM PDT
6. # Fellowship of the Ring: Support Theories for Non-Commutative 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 non-commutative 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
7. # Mini-Course: 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 Riemann-Hilbert 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
8. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
9. # Mini-Course: Introduction to Fluctuations of Beta-Ensembles

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 beta-ensembles, also known as log-gases. 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 log-correlated fields and Gaussian multiplicative chaos.

Updated on Oct 22, 2021 08:18 AM PDT
10. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
11. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:22 PM PDT
12. # Mini-Course: 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 Riemann-Hilbert 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
13. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
14. # Longest Increasing Subsequence and the Schensted Shape of Some Pseudo-Random 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 non-random limit shape and described by Vershik--Kerov and Logan--Shepp. When looking at a sequence of numbers which claims to be "pseudo-random", one could ask whether the longest increasing subsequence and the Schensted shape have similar limits. For most pseudo-random 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
15. # 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
16. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
17. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
18. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
19. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:22 PM PDT
20. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
21. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
22. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:23 PM PDT
23. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
24. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
25. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
26. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
27. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:23 PM PDT
28. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
29. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
30. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
31. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
32. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
33. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
34. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:23 PM PDT
35. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
36. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
37. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:23 PM PDT
38. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
39. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
40. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
41. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
42. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:24 PM PDT
43. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
44. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
45. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
46. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
47. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:24 PM PDT
48. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
49. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
50. # 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 spin-glasses. 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 high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

Updated on Sep 03, 2021 12:24 PM PDT
51. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
52. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
53. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
54. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
55. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
56. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
57. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
58. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
59. # Welcome Tea

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
60. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
61. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
62. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
63. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
64. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
65. # The Analysis and Geometry of Random Spaces - Virtual Participant

Location: MSRI: Online/Virtual
Updated on Apr 07, 2021 10:48 AM PDT
66. # Complex Dynamics: from special families to natural generalizations in one and several variables - Virtual Participant

Location: MSRI: Online/Virtual
Updated on Apr 07, 2021 10:49 AM PDT
1. # 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 African-American research leaders on topics at the forefront of mathematical and statistical research.

Beginning with an intensive two-week 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