Upcoming Events
Due to infection prevention measures, participation in events at the institute is presently not possible for guests.
Many events are currently organized online. Information on how to access these events can be found by clicking “more” below the respective entry.
- Wednesday, 19.05.2021, 10:00 (Online Event)
- Forschungsseminar Mathematische Statistik
Prof. Hannes Leeb, University of Vienna, Österreich:
A (tight) upper bound for the length of confidence intervals with conditional coverage
more ... Location
Online Event
Abstract
We show that two popular selective inference procedures, namely data carving (Fithian et al., 2017) and selection with a randomized response (Tian et al., 2018b), when combined with the polyhedral method (Lee et al., 2016), result in confidence intervals whose length is bounded. This contrasts results for confidence intervals based on the polyhedral method alone, whose expected length is typically infinite (Kivaranovic and Leeb, 2020). Moreover, we show that these two procedures always dominate corresponding sample-splitting methods in terms of interval length.
Further Informations
Der Vortrag findet bei Zoom statt: https://zoom.us/j/159082384
Host
Humboldt-Universität zu Berlin
WIAS Berlin
- Thursday, 20.05.2021, 14:00 (Online Event)
- Seminar Numerische Mathematik
Prof. Thomas Richter, Universität Magdeburg:
Deep neural networks for accelerating fluid-dynamics simulations
more ... Location
Online Event
Further Informations
For zoom login details please contact Alexander Linke linke@wias-berlin.de
Host
WIAS Berlin
- Wednesday, 26.05.2021, 09:00 (Online Event)
- Forschungsseminar Mathematische Statistik
Prof. Hans-Georg Müller, University of California, Davis, USA:
Functional models for time-varying random objects (online talk)
more ... Location
Online Event
Abstract
In recent years, samples of random objects and time-varying object data such as time-varying distributions or networks that are not in a vector space have become increasingly prevalent. Such data can be viewed as elements of a general metric space that lacks local or global linear structure. Common approaches that have been used with great success for the analysis of functional data, such as functional principal component analysis, are therefore not applicable. The concept of metric covariance makes it possible to define a metric auto-covariance function for a sample of random curves that take values in a general metric space and it can be shown to be non-negative definite when the squared semi-metric of the underlying space is of negative type. Then the eigenfunctions of the linear operator with the auto-covariance function as kernel can be used as building blocks for an object functional principal component analysis, which includes real-valued Frechet scores and metric-space valued object functional principal components. Sample based estimates of these quantities are shown to be asymptotically consistent and are illustrated with various data. (Joint work with Paromita Dubey, Stanford University.)
Further Informations
Der Vortrag findet bei Zoom statt: https://zoom.us/j/159082384
Host
Humboldt-Universität zu Berlin
WIAS Berlin
- Wednesday, 26.05.2021, 11:30 (Online Event)
- Seminar Interacting Random Systems
András Tóbiás, TU Berlin:
tba (online talk)
more ... Location
Online Event
Further Informations
Seminar Interacting Random Systems (Online Event)
Host
WIAS Berlin
- May 31 – June 2, 2021 (Online Event)
- Workshop/Konferenz: SPDEs and their friends
more ... Location
Online Event
Host
WIAS Berlin
Technische Universität Berlin
- Tuesday, 08.06.2021, 15:15 (Online Event)
- Oberseminar Nonlinear Dynamics
Chunming Zheng, MPI for Physics of Complex Systems, Dresden:
Transition to synchrony in the three-dimensional Noisy Kuramoto model
more ... Location
Online Event
Abstract
We investigate the transition from incoherence to global collective motion in a three-dimensional swarming model of agents with helical trajectories, subject to noise and global coupling. Without noise this model was recently proposed as a generalization of the Kuramoto model and it was found, that alignment of the velocities occurs for arbitrary small attractive coupling. Adding noise to the system resolves this singular limit.
Host
Freie Universität Berlin
WIAS Berlin
- Wednesday, 09.06.2021, 11:30 (Online Event)
- Seminar Interacting Random Systems
Alexander Zass:
tba (online talk)
more ... Location
Online Event
Further Informations
Seminar Interacting Random Systems (Online Event)
Host
WIAS Berlin
- Monday, 14.06.2021, 15:00 (Online Event)
- Berlin Oberseminar: Optimization, Control and Inverse Problems
Dr. Ozan Öktem, KTH Royal Institute of Technology, Stockholm:
Data driven large-scale convex optimisation
more ... Location
Online Event
Abstract
This joint work with Jevgenjia Rudzusika (KTH), Sebastian Banert (Lund University) and Jonas Adler (DeepMind) introduces a framework for using deep-learning to accelerate optimisation solvers with convergence guarantees. The approach builds on ideas from the analysis of accelerated forward-backward schemes, like FISTA. Instead of the classical approach of proving convergence for a choice of parameters, such as a step-size, we show convergence whenever the update is chosen in a specific set. Rather than picking a point in this set through a handcrafted method, we train a deep neural network to pick the best update. The method is applicable to several smooth and non-smooth convex optimisation problems and it outperforms established accelerated solvers.
Host
WIAS Berlin
- Tuesday, 15.06.2021, 15:15 (Online Event)
- Oberseminar Nonlinear Dynamics
Philipp Lorenz-Spreen, MPI for Human Development, Berlin:
Modeling radicalization dynamics and polarization in temporal networks
more ... Location
Online Event
Abstract
Echo chambers and opinion polarization have been recently quantified in several sociopolitical contexts, across different social media, raising concerns for the potential impact on the spread of misinformation and the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. Here, we propose a model that introduces the phenomenon of radicalization, as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Empirically inspired by the dynamics of social interaction, we consider agents characterized by heterogeneous activities and homophily. We analytically characterize the transition from a global consensus to an emerging radicalization that depends on parameters, which can be interpreted as the controversialness of a topic and the strength of social influence people exert on each other. Finally, we offer a definition of echo-chambers via our model and contrast the model's behavior against empirical data of polarized debates on Twitter, qualitatively reproducing the observed relation between users' engagement and opinions, as well as opinion segregation based on the interaction network. Our findings shed light on the dynamics that may lie at the core of the emergence of echo chambers and polarization in social media.
Host
Freie Universität Berlin
WIAS Berlin
- June 16 – 18, 2021 (Online Event)
- Workshop/Konferenz: Nonlinear Dynamics in Semiconductor Lasers
more ... Location
Online Event
Host
WIAS Berlin
- Tuesday, 22.06.2021, 15:15 (Online Event)
- Oberseminar Nonlinear Dynamics
Bhumika Thakur, Jacobs University, Bremen:
Data driven identification of nonlinear dynamics using sparse regression with applications in plasma physics
more ... Location
Online Event
Abstract
Data driven techniques are increasingly finding applications in physical sciences and plasma physics is no exception. Many plasma processes are highly complex and nonlinear and often the exact form of the equations governing their dynamics is not known. If we can construct these equations from the experimental data, then we can further our understanding of these processes and use techniques such as model reduction to isolate dominant physical mechanisms. A large number of regression techniques are available for identification of system dynamics from data, with varying degrees of generality and complexity. Sparse identification of nonlinear dynamics (SINDy) algorithm is one such technique that can be used to find parsimonious models. I will talk about this algorithm and discuss some examples where it is being applied in plasma physics with a focus on our ongoing attempt at finding the model equations for anode glow oscillations observed in a glow discharge plasma device.
Host
Freie Universität Berlin
WIAS Berlin
- Thursday, 24.06.2021, 14:00 (Online Event)
- Seminar Numerische Mathematik
Felipe Galarce Marín, WIAS Berlin:
Inverse problems in haemodynamics. Fast estimation of blood flows on non-parametric domains from medical data
more ... Location
Online Event
Abstract
This seminar presents a work at the interface between applied mathematics and biomedical engineering. The work's main subject is the estimation of blood flows and quantities of medical interest in diagnosing certain diseases concerning the cardiovascular system. We propose a complete pipeline, providing the theoretical foundations for state estimation from medical data using reduced-order models, and addressing inter-patient variability. Extensive numerical tests are shown in realistic 3D scenarios that verify the potential impact of the work in the medical comunnity.
Further Informations
For zoom login details please contact Alexander Linke linke@wias-berlin.de
Host
WIAS Berlin