# Mathematical Sciences Research Institute

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1. # 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
2. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT

1. # 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
2. # 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 27, 2021 08:09 AM PDT
3. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
4. # 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
5. # 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
6. # Afternoon Tea

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

Location: MSRI: Atrium
Updated on Aug 25, 2021 11:32 AM PDT
8. # Community Detection in Sparse Random Hypergraphs

Location: MSRI: Simons Auditorium, Online/Virtual
Speakers: Yizhe Zhu (University of California, San Diego)

To participate in this seminar, please register HERE.

The stochastic block model has been one of the most fruitful research topics in community detection and clustering. We consider the community detection problem in a sparse random tensor model called the hypergraph stochastic block model. Angelini et al. (2015) conjectured a threshold for detecting the community structure in this model, and we confirmed the positive part of the phase transition in the 2-block case. We introduced a matrix that counts self-avoiding walks on random hypergraphs, whose leading eigenvectors give a correlated reconstruction of the community. Based on joint work with Soumik Pal.

Updated on Oct 27, 2021 11:45 AM PDT
9. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
10. # 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
11. # Seminar TBD

Location: MSRI: Simons Auditorium, Online/Virtual
Speakers: Dan Romik (University of California, Davis)

To participate in this seminar, please register HERE.

Updated on Oct 26, 2021 12:50 PM PDT
12. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
13. # Seminar TBD

Location: MSRI: Simons Auditorium, Online/Virtual
Speakers: Pablo Ferrari (University of Buenos Aires)

To participate in this seminar, please register HERE.

Updated on Oct 26, 2021 12:53 PM PDT
14. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
15. # 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
16. # KPZ Equation with a Small Noise, Deep Upper Tail and Limit Shape

Location: MSRI: Simons Auditorium, Online/Virtual
Speakers: Yier Lin (Columbia University)

To participate in this seminar, please register HERE.

We prove an exact limit shape of the KPZ equations under weak noise scaling, when we condition the value of the KPZ equations at one point to be very large. We also show that under such conditioning, the shape of the noise in the KPZ equation is asymptotically given by the optimizer of the L^4 Gagliardo-Nirenberg-Sobolev inequality.

Updated on Oct 26, 2021 12:56 PM PDT
17. # Afternoon Tea

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
18. # Invariant Measures for Multilane Exclusion Process

Location: MSRI: Simons Auditorium, Online/Virtual
Speakers: Ellen Saada (CNRS, MAP5 lab.)

To participate in this seminar, please register HERE.

We consider the simple exclusion process on $\Z\times\{0,1\}$, that is,  an horizontal  ladder'' composed of $2$ lanes. Particles can jump according to a lane-dependent translation-invariant nearest neighbour jump kernel, i.e. horizontally'' along each lane, and vertically'' along the scales of the ladder. We analyze the set of extremal invariant measures for this model. (Joint work with Gidi Amir, Christophe Bahadoran and Ofer Busani. ArXiv : 2105.12974

Updated on Oct 26, 2021 12:58 PM PDT
19. # Program Associates' Seminar

Location: MSRI: Simons Auditorium, Online/Virtual

To participate in this seminar, please register HERE.

Updated on Oct 26, 2021 01:00 PM PDT
20. # Afternoon Tea

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

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

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
23. # 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
24. # Afternoon Tea

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

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

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

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

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

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

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

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
33. # 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
34. # Afternoon Tea

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

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

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

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
38. # 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
39. # Afternoon Tea

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

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

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

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
43. # 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
44. # Afternoon Tea

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

Location: MSRI: Atrium
Updated on Aug 24, 2021 11:21 AM PDT
46. # 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
47. # Afternoon Tea

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

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

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

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

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

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

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

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

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

Location: MSRI: Online/Virtual
Updated on Apr 07, 2021 10:48 AM PDT
62. # 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

1. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
2. # SeminarMini-Course: The Quest for Fredholm Determinants

Updated on Oct 19, 2021 02:58 PM PDT
3. # SeminarFellowship of the Ring: Support Theories for Non-Commutative Complete Intersections

Updated on Oct 20, 2021 11:40 AM PDT
4. # SeminarRandom Matrices and Random Landscapes

Updated on Sep 03, 2021 12:21 PM PDT
5. # SeminarPositivity and Universality (from a Combinatorial Perspective)

Updated on Oct 21, 2021 09:24 AM PDT
6. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
7. # SeminarMini-Course: Introduction to Fluctuations of Beta-Ensembles

Updated on Oct 22, 2021 08:17 AM PDT
8. # SeminarWelcome Tea

Updated on Aug 25, 2021 11:32 AM PDT
9. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
10. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
11. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
12. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
13. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
14. # SeminarWelcome Tea

Updated on Aug 25, 2021 11:32 AM PDT
15. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
16. # SeminarProgram Associates' Seminar

Updated on Oct 04, 2021 10:56 AM PDT
17. # SeminarAvoiding Local Parametrix Problems in Riemann-Hilbert Theory

Updated on Oct 08, 2021 12:40 PM PDT
18. # SeminarSpectral Theory of Non-Self-Adjoint Dirac Operators on the Circle

Updated on Oct 08, 2021 11:06 AM PDT
19. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
20. # SeminarMini-Course: Correlation Functions of the Sinh-Gordon Quantum Field Theory in 1+1 Dimensions Part II

Updated on Oct 13, 2021 10:10 AM PDT
21. # SeminarRandom Matrices and Random Landscapes

Updated on Sep 03, 2021 12:17 PM PDT
22. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
23. # SeminarIndependence Preserving Transformations and Exact Solvability

Updated on Oct 08, 2021 10:52 AM PDT
24. # SeminarColloquium: Topological Expansion and Phase Diagram for Ensembles of Random Matrices with Complex Potentials

Updated on Oct 08, 2021 10:27 AM PDT
25. # SeminarIntegrable Probability Open Problem Session

Updated on Oct 04, 2021 10:49 AM PDT
26. # SeminarAfternoon Tea

Updated on Aug 24, 2021 11:21 AM PDT
27. # SeminarMini-Course: Correlation Functions of the Sinh-Gordon Quantum Field Theory in 1+1 Dimensions Part I

Updated on Oct 13, 2021 10:12 AM PDT
28. # SeminarFellowship of the Ring: The Fiber-Full Scheme

Updated on Oct 11, 2021 01:11 PM PDT
29. # SeminarRandom Matrices and Random Landscapes

Updated on Sep 03, 2021 12:15 PM PDT
30. # SeminarColloquium: A Survey of Results for Asymptotics of Determinants of Operators

Updated on Oct 08, 2021 11:35 AM PDT
There are more then 30 past seminars. Please go to Past seminars to see all past seminars.