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Colloquia & Seminars

All Seminars

Postdoc Seminars

Graduate Seminars

Other Colloquia & Seminars



Current Seminars

  1. Afternoon Tea

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

Upcoming Seminars

  1. UIRM Five Minute Talks

    Location: MSRI: Online/Virtual, Simons Auditorium

    To participate in this seminar, please register HERE.

    To view all five minute talk videos and slides, go to this PAGE.

    Members of the Universality and Integrability in Random Matrix Theory and Interacting Particle Systems program will give 5-minute presentations about their research interests. 

    Updated on Sep 03, 2021 09:29 AM PDT
  2. Algebraic Approach to Stochastic Duality for Markov Processes with Some Examples and Applications

    Location: MSRI: Simons Auditorium, Online/Virtual
    Speakers: Chiara Franceschini (Instituto Superior Técnico)

    To participate in this seminar, please register HERE.

    In this talk I will introduce the concept of duality for Markov processes and explain how duality relations can be constructed starting from the description of the infinitesimal generator of the process with generators of a suitable Lie algebra. I will provide some classical examples as well as more recent results and also show some applications in the context of interacting particle systems.

    Updated on Sep 03, 2021 03:01 PM PDT
  3. Professional Development Seminar

    Location: MSRI: Simons Auditorium, Online/Virtual

    To participate in this seminar, please register HERE.

    Topic -- Public speaking and how to give a colloquium.

    Updated on Sep 15, 2021 02:44 PM PDT
  4. Program Associates' Seminar

    Location: MSRI: Simons Auditorium, Online/Virtual

    To participate in this seminar, please register HERE.

    Updated on Sep 03, 2021 03:10 PM PDT
  5. Meet the Staff

    Location: Front Courtyard
    Updated on Sep 15, 2021 03:06 PM PDT
  6. Welcome Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  8. 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 12:07 PM PDT
  9. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Sep 08, 2021 11:17 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 12:14 PM PDT
  12. Afternoon Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  16. 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 12:14 PM PDT
  17. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 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 12:14 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. Welcome Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  24. 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 12:15 PM 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. 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 12:15 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. Welcome Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  32. 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 12:15 PM PDT
  33. Afternoon Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  35. 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 12:17 PM PDT
  36. Afternoon Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  40. 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 12:17 PM PDT
  41. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 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 12:20 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. Welcome Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  51. 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
  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. Welcome Tea

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

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

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

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

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

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

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  71. 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
  72. Afternoon Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  74. 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
  75. Afternoon Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  79. 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
  80. Afternoon Tea

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  84. 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
  85. Afternoon Tea

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  87. 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
  88. Afternoon Tea

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

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

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

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

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

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

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

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

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

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

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

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

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

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
No upcoming events under African Diaspora Joint Mathematics Workshop

Past Seminars

  1. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  2. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 09:28 AM PDT
  3. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  4. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  5. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 11:03 AM PDT
  6. Seminar Welcome Tea

    Updated on Aug 25, 2021 11:32 AM PDT
  7. Seminar Afternoon Tea

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  8. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 09:26 AM PDT
  9. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  10. Seminar Afternoon Tea

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  11. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  12. Seminar Afternoon Tea

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  13. Seminar UIRM Five Minute Talks

    Updated on Aug 27, 2021 09:03 AM PDT
  14. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
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