Presentations

2022

• Augmenting Bayesian inference with possibility theory

  Presentation of Jérémie Houssineau, University of Warwick, UK [PDF]

2020

• Advancements in Hybrid Iterative Methods for Inverse Problems

  Presentation of Julianne Chung, Virginia Tech, Blcksburg, USA [PDF]

• Challenges in Dynamical Systems Inference: New Approaches for Parameter and Uncertainty Estimation

  Presentation of Matthias Chung, Virginia Tech, Blcksburg, USA [PDF]

• Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

  Presentation of Marc Bocquet, École des Ponts Paris Tech, France [PDF]

• Implicit equation-free methods applied on noisy slow-fast systems

  Presentation of Anna Dittus, Universität Rostock, Germany [PDF]

• Posterior Inference for Sparse Hierarchical Non-stationary Models

  Presentation of Lassi Roininen, Lappeentanta University of Technology, Finland

• Computational and data-driven methods for large-scale inverse problems

  Presentation by Tristan van Leeuwen, Universiteit Utrecht, The Netherlands

2019

• Deterministic Sequential Monte Carlo for non-Gaussian elliptic problems

  Presentation of Sangeetika Ruchi, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands [PDF]

• Stability and uniform fluctuation estimates of Ensemble Kalman-Bucy Filters

  Presentation of Pierre del Moral, INRIA, Bordeaux Research Center, University of Bordeaux, France [PDF]

• Assimilating data with outer probability measures

  Presentation by Jeremie Houssienieau, The University of Warwick, UK [PDF]

• A Downscaling Data Assimilation Algorithm

  Presentation by Edriss Titi, University of Cambridge, Texas A&M University and Weizmann Institute of Science [PDF]

• Non-linear functionals preserving normal distribution and their asymptotic normality

  Presentation by Linda Khachatryan, Institute of Mathematics of the National Academy of Science of RA, Armenia [PDF]

• Probabilistic Linear Solvers

  Presentation by Jon Cockayne, University of Warwick, UK [PDF]

• Kalman-Wasserstein Gradient Flows

  Presentation by Franca Hoffmann, California Institute of Technology, USA [PDF]

• Applied Data Assimilation: Diabetes phenotyping/forecasting + Hybrid machine learning approaches

  Presentation by Matthew Levine, California Institute of Technology, USA [PDF]

• Ensemble Data AssimilationforCoupledModels oftheEarth System

  Presentation by Lars Nerger, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Germany [PDF]

• Data assimilation analysis for a stochastic one-layer rotating shallow water system driven by transport noise

  Presentation by Dana Lang, Imperial College London, UK [PDF]

2018

• On stability of a class of Kalman - Bucy filters for systems with non-linear dynamics

  Presentation by Toni Karvonen, Aalto University, Finland [PDF]

• Composing stochastic quasi-Newton-Type algorithms

  Presentation by Thomas Schön, Uppsala University, Sweden [PDF]

• Localization for high dimensional data assimilation and MCMC

  Presentation by Xin T. Tong, National University of Singapore, Singapore [PDF]

• A causal approach to circulation variability in the Southern Hemisphere

  Presentation by Elena Saggioro, University of Reading and Imperial College London, UK [PDF]

• Dancing with Drones

  Presentation by Erin Linebarger, University of Utah, USA, and visiting research fellow in the SFB 1294 [PDF]

2017

• Minimization-based sampling from the posterior distribution for inverse problems

  Presentation by Dean Oliver, Uni Centre for Integrated Petroleum Research, Bergen, Norway [PDF]

• Modelling subsidence: On the use of the particle filter for geomechanical parameter estimation

  Presentation by Femke Vossepoel, Delft University of Technology [PDF]

• A class of nonlinear filters induced by local couplings

  Presentation by Alessio Spantini, Massachusetts Institute of Technology (MIT), USA [PDF]

2026

2025

• SpringSchool_2025_Schedule.pdf

2023

• Slides Lecture Gilles Blanchard

  Prediction with expert advice under budget constraints

• Slides Lecture Katharina Baum

  Integrating multi-layered data and prior knowledge into machine learning

• Slides Lecture Niklas Kaspareit

  Robo Lab - A Search and Rescue Mission

2022

• Ralf Metzler Lectures

  Brown, Einstein, Smoluchowski & beyond

• Aretha Teckentrup Lecture 1

  Bayesian inference for complex models

• Aretha Teckentrup Lecture 2
• Aretha Teckentrup Lecture 3
• Infrastructure project input

  Data management and UP Toolbox

• Infrastructure Project input

  RDMo - Research Data Management Organizer

2019

• Adele Diederich - Lecture 1

  Multi-stage sequential sampling models: A framework for binary choice options - Part 1

• Adele Diederich - Lecture 2

  Multi-stage sequential sampling models: A framework for binary choice options - Part 2

• Adele Diederich - Lecture 3

  Multi-stage sequential sampling models: A framework for binary choice options - Part 3

• Adele Diederich - Lecture 4

  Multi-stage sequential sampling models: A framework for binary choice options - Part 4

• John Harlim - Lecture 1

  Diffusion Maps: A manifold learning algorithm

• John Harlim - Lecture 2

  Data-Driven Nonparametric Likelihood Functions

• John Harlim - Lecture 3

  Data-Driven Nonaparametric Likelyhood Functions: Continue

• John Harlim - Lecture 4

  Nonparametric modeling of dynamical systems

• Marc Hoffmann - Lecture 1

  Statistical inference for structured models Part I: Some connections between nonparametic estimations ans PDEs - the stochastic models

• Marc Hoffmann - Lecture 2

  Part II: Example 3 (Human population models). Statistical methodology

• Marc Hoffmann - Lecture 3

  Part III: Lepski's principle. Estimation in bifurcating models.

• Marc Hoffmann - Lecture 4

  Part IV: Estimation with bias sampling and proxy experiments. Large population models. Further models

2018

• David Dereudre - Lectures 1 and 2

  Introduction to the Theory of Gibbs Point Process - Finite Volume Gibbs Point Process

• David Dereudre - Lecture 3

  Introduction to the Theory of Gibbs Point Process - Estimation of parameters

• David Dereudre - Lecture 4

  Introduction to the Theory of Gibbs Point Process

• Youssef Marzouk - Lectures 1 and 2

  Bayesian inference and MCMC foundations

• Youssef Marzouk - Lecture 3

  Bayesian modeling and computation for inverse problems

• Youssef Marzouk - Lecture 4

  Posterior approximations for Bayesian inverse problems

• Youssef Marzouk - Lecture 5

  Bayesian optimal experimental design

• Carola Schönlieb - Lecture 1

  Topics in Mathematical Imaging - Variational models & PDEs for imaging by examples

• Carola Schönlieb - Lecture 2

  Topics in Mathematical Imaging - Derivation of these models & analysis

• Carola Schönlieb - Lecture 3

  Topics in Mathematical Imaging - Numerical solution

• Carola Schönlieb - Lecture 4

  Topics in Mathematical Imaging - Some machine leaning connections

2023

• Multilevel Monte Carlo Methods for Parametric Expectations: Distribution and Robustness Measures

  Presentation by Sebastian Krumscheid joint work with: Q. Ayoul-Guilmard, S. Ganesh, and F. Nobile [PDF]

• Sampling with Stein Discrepancies

  Presentation by Cris. J. Oats [PDF]

• Likelihood Inference for SDE-models with applications in Biology and Geophysics

  Presentation by Michael Sørensen [PDF]