Upcoming Events

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Monday, 23.04.2018, 14:00 (WIAS-ESH)
Seminar Quantitative Biomedizin
Prof. S. Waters, University of Oxford, GB:
Fluid dynamical models for tissue engineering
more ... Location
Weierstraß-Institut, Mohrenstr. 39, 10117 Berlin, Erdgeschoss, Erhard-Schmidt-Hörsaal

Abstract
Tissue engineers aim to grow tissues in vitro to replace those in the body that have been damaged through age, trauma or disease. A common approach is to seed cells within a scaffold which is then cultured in a bioreactor. The key challenge it to provide the appropriate mechanical and biochemical cellular environment that promotes tissue growth in vitro. Fluid flows have a key role to play in addressing this challenge, as they can provide mechanical cues to cells (e.g. via fluid shear and pressure), and enhance the delivery of nutrients and growth factors (via advection). In this talk, I will explore how mechanistic mathematical models, in combination with state-of-the art experimental studies, can provide quantitative insights into the interplay between the fluid flows and the resulting tissue growth. Quantitative understanding of this interplay offers the exciting potential to manipulate the experimental design (e.g. scaffold porosity, bioreactor operating conditions) to establish defined mechanical cues that enhance the generation of complex 3D tissues in vitro.

Host
WIAS Berlin
Wednesday, 25.04.2018, 10:00 (WIAS-ESH)
Forschungsseminar Mathematische Statistik
N. Baldin, University of Cambridge, GB:
Optimal link prediction with matrix logistic regression
more ... Location
Weierstraß-Institut, Mohrenstr. 39, 10117 Berlin, Erdgeschoss, Erhard-Schmidt-Hörsaal

Abstract
In this talk, we will consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices. The generative model is formulated as a matrix logistic regression. The performance of the model is analysed in a high-dimensional regime under a structural assumption. The minimax rate for the Frobenius-norm risk is established and a combinatorial estimator based on the penalised maximum likelihood approach is shown to achieve it. Furthermore, it is shown that this rate cannot be attained by any (randomised) algorithm computable in polynomial time under a computational complexity assumption. (joint work with Q. Berthet)

Further Informations
Forschungsseminar “Mathematische Statistik”

Host
Humboldt-Universität zu Berlin
Universität Potsdam
WIAS Berlin