# Presentations

Here you can find the presentations from the SFB events such as Colloquia and Seminars. The presentations are being made available for the public with permission of the speakers.

## Colloquia

## 2023

**Conservative SPDEs as fluctuating mean field limits of stochastic gradient descent**Presentation by Vitalii Konarovskyi, University of Bielefeld, Germany [PDF]

**Understanding and Applying Reinforcement Learning**Presentation by Claire Vernade, University of Tuebingen, Germany [PDF]

**Model Order Reduction in Data Assimilation**Presentation by Karen Veroy-Grepl, Eindhoven University of Technology, Netherlands [PDF]

**Reduced Basis Methods: From Key Ingredients to 4DVar**Presentation by Martin Grepl, RWTH Aachen, Germany [PDF]

## 2022

**Machine Learning in the context of drift**Presentation by Barbara Hammer, University of Bielefeld, Germany [PDF]

**Collective dynamics in the social and data science**Presentation by Marie-Therese Wolfram, University of Warwick, UK [PDF]

**Challenges of data driven modelling in cardiac dynamics**Presentation by Ulrich Parlitz, University of Göttingen, Germany [PDF]

**Using 'Data-Intelligence' to Understand the Physics of the Inner Magnetosphere**Presentation by Geoff Reeves, Los Alamos National Laboratory, US [PDF]

**Time scales in early warnings: a probabilistic approach**Presentation by Susanne Ditlevsen, Department of Mathematical Sciences, University of Copenhagen, Denmark [PDF]

## 2021

**Coarse-graining of complex systems with parameter uncertainties**Presentation by Carsten Hartmann, Brandenburgische Technische Universität Cottbus-Senftenberg, Germany [PDF]

**Understanding and predicting regional to global biodiversity dynamics**Presentation by Damaris Zurell, University of Potsdam, Germany [PDF]

**Analyzing Multi-Messenger Astronomy Data to reveal fundamental Properties of the Cosmos**Presentation by Tim Dietrich, University of Potsdam, Germany [PDF]

## 2020

**Optimal Sensor Placement for the Quantification of Model Uncertainty A functional analysis perspective**Presentation by Karen Veroy-Grepl, Eindhoven University of Technology, The Netherlands [PDF]

**Statistical properties of deterministic dynamical systems and their applications in weather and climate forecasting**Presentation by Georg Gottwald, The University of Sydney, Australia [PDF]

**Digital first, concerns second: Our power to change everyday life and the of responsibility in algorithm developmet**Presentation by Ulrike Lucke, University of Potsdam [PDF]

**Posterior consistency in Bayesian inference with exponential priors**Presentation by Masoumeh Dashti, University of Sussex, UK [PDF]

**Cognitive computational neuroscience of vision**Presentation by Nikolaus Kriegskorte, Zuckerman Mind Brain Behavior Institute, Columbia University, USA [PDF]

**Bayesian estimation of nonlinear Hawkes processes**Presentation by Judith Rousseau, Oxford University, UK [PDF]

## 2019

**Specification of the Near-Earth Space Environment using Data Assimilation Techniques**Presentation by Ludger Scherliess, Utah State University, USA [PDF]

**Effective behavior of random media**Presentation by Felix Otto, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany [PDF]

**All-at-once versus reduced formulations of inverse problems and their regularization**Presentation by Barbara Kaltenbacher, Alpen-Adria-Universität Klagenfurt, Austria [PDF]

**Implementation of an iterative ensemble smoother for big-data assimilation and reservoir history matching**Presentation by Geir Evensen, NORCE–Norwegian Research Center and Nansen Environmental and Remote Sensing Center, Norway [PDF]

**Distances for discretely observed jump processes and applications in nonparametric statistics**Presentation by Ester Mariucci, Otto von Guericke Universität, Magdeburg, Germany [PDF]

**Low dimensional approximation of weak constraint variational data assimilation**Presentation by Melina Freitag, University of Potsdam, Germany

## 2018

**Approximate Kernel Embeddings of Distributions**Presentation by Dino Sejdinovic, University of Oxford, UK [PDF]

**Numerical Analysis in Visual Computing: what we can learn from each other**Presentation by Uri Ascher, University of British Columbia, Canada [PDF]

**The Epidemic Type Aftershock Sequence (ETAS) Model**Presentation by Maximillian Werner, University of Bristol, UK

**Piecewise-Deterministic MCMC**Presentation by Arnaud Doucet, University of Oxford, UK

**Image-based modelling of problems in cell motility**Presentation by Till Brettschneider, University of Warwick, UK [PDF]

## 2017

**Distributional Uncertainty in Uncertainty Quantification**Presentation by Tim Sullivan, Zuse Institute Berlin and Freie Universität zu Berlin, Germany [PDF]

**Comparing deep neural networks against humans: Object recognition with weak signals**Presentation by Felix Wichmann, Universität Tübingen, Germany [PDF]

## Seminars

## 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]

## 2022

**Augmenting Bayesian inference with possibility theory**Presentation of Jérémie Houssineau, University of Warwick, UK [PDF]

## Kalman Lectures

## 2019 and more recent

The video presentations from the Kalman Lectures in 2019 and onwards were videotaped and can be found here.

## 2018

**1st Kalman Lecture by Andrew Stuart**The first Kalman Lecture took place on the 24th of August 2018, where Andrew Stuart from Caltech talked about the "The Legacy of Rudolf Kalman".

## Spring Schools

## 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

**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

## DA Days

## 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]

## Joint Workshop

Here you can find the presentations of the speakers from the joint workshop on Conservation Principles, Data and Uncertainty in Atmosphere-Ocean Modelling, who sent us their presentation and allowed it to be made public. The talks are organized by workshop days/themes and in alphabetical order.

## Day 1: Energy budgets and energy transfer in climate models and data (Patron: TRR181)

**Oceanic overturning and heat transport: The role of background diffusivity**Presentation by Jonas Nycander, Stockholm University, Sweden [PDF]

**Observing Earth's Energy Imbalance**Presentation by Till Kuhlbrodt, NCAS, University of Readings, UK [PDF]

## Day 2: Data assimilation (Patron: SFB 1294)

**Reanalysis of radiation belt electrons relying on a Kalman filter, four spacecraft, and a diffusion model**Presentation by Sebastian Cervantes, GeoForschungsZentrum and University of Potsdam, Germany [PDF]

**On the geometry of Stein variational gradient descent (SVGD)**Presentation by Nikolas Nüsken, University of Potsdam, Germany [PDF]

**Parameter Estimation in Size-Structured Aerosol Populations using Bayesian State Estimation**Presentation by Matthe Ozon, University of Eastern Finland, Finland [PDF]

**State and parameter estimation from observed signal increments**Presentation by Paul Rozdeba, University of Potsdam, Germany [PDF]

**Stabilizing Unstable Flows by Coarse Mesh Observables and Actuators - A Pavement to Data Assimilation**Presentation by Edriss S. Titi, University of Cambridge, Texas A&M University and Weizmann Institute of Science [PDF]

**Downscaling Data Assimilation Techniques Applied to Low Froude Number Shallow Water Flows**Presentation by Stefan Vater, Freie Universität Berlin, Germany [PDF]

## Day 3: Stochastic modelling in atmosphere-ocean science (Patron: SFB 1114)

**Improving stochastic parametrisation schemes using high-resolution model simulations**Presentation by Hannah Christensen, University of Oxford, UK [PDF]

**Finite-time breakdown of chemical precipitation patterns**Presentation by Marcel Oliver, Jacobs University, Germany [PDF]

**Dynamics under location uncertainty and other energy-related stochastic subgrid schemes**Presentation by Valentin Resseguir, IRISA, France [PDF]

**A fluctuation-dissipation relation for the ocean subject to turbulent atmospheric forcing**Presentation by Achim Wirth from MEIGE/LEGI/CRNS, France [PDF]

**A consistent framework for stochastic representation of large-scale geophysical ows**Presentation by Etienne Mémin, INRIA/Irmar, France