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. 

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.

To participate in this seminar, please register HERE.

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

To participate in this seminar, please register HERE.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.