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Upcoming Colloquia & Seminars

  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
  3. 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
  4. 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
  5. Afternoon Tea

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

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  13. 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
  14. Afternoon Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  18. 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
  19. Afternoon Tea

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

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 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 12:24 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. 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
  42. Afternoon Tea

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

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

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

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

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

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

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

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