Uploaded November 2024 | Updated September 2026, 2 weeks ago
Raphael Winter is visiting OIST from 2024-08-07 until 2024-10-28 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Title: How Much of Plasma Physics Can a Mathematician Understand?
Abstract: The kinetic theory of plasma is a well-developed theory in physics which fuels our understanding of nature, but also drives technological innovation such as fusion reactors. Due to the long-range interaction of charged particles (decays like 1/|x|), the dynamics of such systems is particularly rich, but also difficult to capture in mathematically precise theorems. In this talk we give an overview of how far mathematicians have come in this area, what the current challenges are, and what one can hope to be able to do in the future.
Profile: Raphael Winter is an assistant professor in Mathematical Analysis at Cardiff University. He mainly works on Partial Differential Equations describing the kinetic theory of gases and plasmas, as well as their derivation from interacting particle systems. Recent works include the stopping power law for ions, collisional relaxation of plasma and the regularity of the Landau-Coulomb equation.
#OIST #OIST_TSVP #Fusion #Plasma #Mathematics #Physics #Theoretical #Science #VisitingProgram #Okinawa #TSVP
Raphael Winter is visiting OIST from 2024-08-07 until 2024-10-28 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Title: How Much of Plasma Physics Can a Mathematician Understand?
Abstract: The kinetic theory of plasma is a well-developed theory in physics which fuels our understanding of nature, but also drives technological innovation such as fusion reactors. Due to the long-range interaction of charged particles (decays like 1/|x|), the dynamics of such systems is particularly rich, but also difficult to capture in mathematically precise theorems. In this talk we give an overview of how far mathematicians have come in this area, what the current challenges are, and what one can hope to be able to do in the future.
Profile: Raphael Winter is an assistant professor in Mathematical Analysis at Cardiff University. He mainly works on Partial Differential Equations describing the kinetic theory of gases and plasmas, as well as their derivation from interacting particle systems. Recent works include the stopping power law for ions, collisional relaxation of plasma and the regularity of the Landau-Coulomb equation.
#OIST #OIST_TSVP #Fusion #Plasma #Mathematics #Physics #Theoretical #Science #VisitingProgram #Okinawa #TSVP







![Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)
[Title]
Florian Yger - A Geometric Adventure in Machine Learning: Learning with Invariances, Structures, and Prior Knowledge (TSVP Talk at OIST)
[Description]
Florian Yger is visiting OIST from 2024-05-20 until 2024-08-15 through the Theoretical Sciences Visiting Program (TSVP). Find out more about the TSVP on the program website:
https://www.oist.jp/visiting-program.
Abstract: In this presentation, we embark on a journey through the landscapes of machine learning, focusing on the role of representation learning. Traditional approaches often restrict themselves to Euclidean spaces, yet many real-world data, such as graphs and covariances, defy this simplistic framework. We explore how embracing non-Euclidean geometries—specifically curved spaces like Riemannian manifolds—unlocks new possibilities in understanding and predicting structured data. This framework allows for the incorporation of prior knowledge such as constraints and invariances into machine learning algorithms.
Central to our exploration is the Fréchet averaging problem, a fundamental tool that generalizes the well-know average to metric spaces. From this cornerstone, we derive many extensions ranging from graph averaging to dimensionality reduction on Riemannian manifolds. We illustrate their practical implications through numerical experiments on biomedical data.
Motivated by challenging applications, this presentation not only highlights the limitations of Euclidean-centric approaches but also underscores the potential of geometry-aware representation learning. Join us as we navigate this geometric adventure into the curved realm of geometry-aware representation learning.
Profile: Florian Yger is an associate professor at Université Paris-Dauphine since 2015, teaching Data Analysis and Machine Learning in the department MIDO. Within the LAMSADE, he is part of the team MILES which focuses on trustworthy Machine Learning and Explainable AI. From 2014 to 2015, he was a JSPS postdoctoral fellow in the laboratory of Prof. Sugiyama at Tokyo University. He received his PhD in Computer science from LITIS, Université de Rouen under the supervision of Alain Rakotomamonjy in 2013. He contributes to the problem of representation learning with a particular interest in the representation of structured data (graphs, covariance matrices,…) and the development of learning algorithm for non-Euclidean spaces. This work has many applications ranging from signal processing (EEG signals and Brain Computer Interface) and to image processing (Paintings for art style recognition). More recently, within MILES teams, he addresses the questions of trust, explainability and interpretability in machine learning models with a focus counterfactual reasoning on data. He recently started studying the interplay between computational social choice and machine learning in the context of voter’s opinion aggregation. He is a visiting researcher at RIKEN AIP, Japan since 2017, is a member of the Prairie (PaRis Artificial Intelligence Research InstitutE) where he holds a junior chair, and an Affiliate of the Theoretical Sciences Visiting Program (TSVP) at OIST.
#OIST #OIST_TSVP #MachineLearning #Geometry #Manifolds #Theoretical #Science #VisitingProgram #Okinawa #TSVP Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)](https://i.ytimg.com/vi/KlU1mS1xr3I/mqdefault.jpg)


