Current Seminars

Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT
Upcoming Seminars

Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:07 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Sep 08, 2021 11:17 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:14 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:14 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:14 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:15 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:15 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:15 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:17 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:17 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:20 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:21 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:22 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:22 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:23 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Random Matrices and Random Landscapes
Location: MSRI: Simons Auditorium, Online/Virtual
UC Berkeley, 740 Evans Hall Speakers: Gérard Ben Arous (New York University, Courant Institute)To register for this course, go to: https://www.msri.org/seminars/26228
This class aims at understanding some important classes of smooth random functions of very many variables.
What can be said about the complexity of the topology of the landscapes they define?
How efficient are the natural exploration or optimization algorithms in these landscapes?
The toolbox of Random Matrix Theory will be used for both questions.
We will concentrate on two wide classes of interesting smooth random functions of many variables.
A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spinglasses. There the randomness is assumed to model quenched disorder in the medium.
Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in highdimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.
Updated on Sep 03, 2021 12:24 PM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Welcome Tea
Location: MSRI: AtriumUpdated on Aug 25, 2021 11:32 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
Afternoon Tea
Location: MSRI: AtriumUpdated on Aug 24, 2021 11:21 AM PDT 
The Analysis and Geometry of Random Spaces  Virtual Participant
Location: MSRI: Online/VirtualUpdated on Apr 07, 2021 10:48 AM PDT 
Complex Dynamics: from special families to natural generalizations in one and several variables  Virtual Participant
Location: MSRI: Online/VirtualUpdated on Apr 07, 2021 10:49 AM PDT
Past Seminars

Seminar Meet the Staff
Updated on Sep 15, 2021 03:06 PM PDT 
Seminar Program Associates' Seminar
Updated on Sep 17, 2021 09:16 AM PDT 
Seminar Professional Development Seminar
Updated on Sep 15, 2021 02:44 PM PDT 
Seminar Algebraic Approach to Stochastic Duality for Markov Processes with Some Examples and Applications
Updated on Sep 03, 2021 03:01 PM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar The Charm of Integrability: From Nonlinear Waves to Random Matrices
Updated on Sep 09, 2021 10:00 AM PDT 
Seminar UIRM Five Minute Talks
Updated on Sep 03, 2021 09:28 AM PDT 
Seminar Integrable Probability Open Problems Session
Updated on Sep 10, 2021 08:20 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar MiniCourse: Interacting Particle Systems and SPDEs
Updated on Sep 03, 2021 01:42 PM PDT 
Seminar Fellowship of the Ring: Vanishing of Local Cohomology Modules
Updated on Sep 13, 2021 11:00 AM PDT 
Seminar Random Matrices and Random Landscapes
Updated on Sep 14, 2021 10:17 AM PDT 
Seminar Riemann Hilbert Open Problems Session
Updated on Sep 08, 2021 10:06 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Program Associate Short Talks
Updated on Sep 03, 2021 11:49 AM PDT 
Seminar On Some Models of Last Passage Percolation and Their Scaling Limits
Updated on Sep 03, 2021 11:43 AM PDT 
Seminar UIRM Five Minute Talks
Updated on Sep 03, 2021 11:03 AM PDT 
Seminar Welcome Tea
Updated on Aug 25, 2021 11:32 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Random Matrix Theory Open Problem Session
Updated on Sep 03, 2021 09:38 AM PDT 
Seminar The SixVertex Model and Random Matrix Theory
Updated on Sep 03, 2021 10:09 AM PDT 
Seminar UIRM Five Minute Talks
Updated on Sep 03, 2021 09:26 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar MiniCourse: Interacting particle systems and SPDEs
Updated on Sep 03, 2021 01:40 PM PDT 
Seminar Random Matrices and Random Landscapes
Updated on Sep 14, 2021 10:14 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Afternoon Tea
Updated on Aug 24, 2021 11:21 AM PDT 
Seminar Program Associates' Seminar
Updated on Sep 03, 2021 01:10 PM PDT