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05.05.2021 13:00 Felix Dietrich, TUM Wissenschaftliches Rechnen:
Modelling human crowds from first principles and through machine learning methodsvia ZOOM (Boltzmannstr. 3, 85748 Garching)

Large human crowds constitute a fascinating research area for mathematical modelling. Even though the individuals forming the crowd are extremely complex, dominant emergent behaviors like density-velocity profiles and lane formation can be captured with relatively simple models. In this talk, we discuss recent "first principle", agent-based approaches, coarser cellular automatons, as well as modelling ideas from fluid dynamics. We also show how machine learning methods can provide a separate path to understanding crowds, with both strengths and shortcomings compared to the traditional approach.

10.05.2021 15:00 Yuanzhoa Zhang (Cornell University):
TBAVirtuelle Veranstaltung (Boltzmannstr. 3, 85748 Garching)

tba

10.05.2021 16:00 Pierre Tarrès (NYU Shanghai):
TBA(using zoom) (Parkring 11, 85748 Garching-Hochbrück)

TBA

12.05.2021 13:00 Aurélien Tellier, TUM School of Life Sciences :
Using full genome (and epigenome) data to infer past species history and ecological/life-history traitsMI 02.08.011 (Boltzmannstr. 3, 85748 Garching)

The field of evolutionary genetics is profoundly rooted in stochastic mathematical theory and since several years the theory has been extended to model the evolution of full genomes. Indeed, large amount of full genome data are becoming available for human but also non-model organisms. Several methods based on the Sequential Markovian coalescent (SMC) have been developed to use sequence data to uncover population demographic history. While these methods can be applied in principle to all possible species, they have been developed based on the human biological characteristics and have main limitations such as assuming sexual reproduction and no overlap of generations. However, in many plants, invertebrates, fungi and other taxa, these assumptions are often violated due to different ecological and life history traits, such as self-fertilization, long term dormant structures (seed or egg-banking) or large variance in offspring production. I will first describe a novel SMC-based method which we developed to infer 1) the rates of seed/egg-bank and of self-fertilization, and 2) the populations' past demographic history. We also apply our method to Arabidopsis thaliana, Daphnia pulex and to detect seed banking in different populations of the wild tomato species Solanum chilense. Finally, I will show that we can even extend this method to detect and to date the changes of selfing / seed banking in time. I will conclude by discussing more general class of mathematical stochastic models and methods which should be developed for applicability to all species.

17.05.2021 15:00 Mazyar Ghani:
TBAVirtuelle Veranstaltung (Boltzmannstr. 3, 85748 Garching)

TBA

17.05.2021 16:00 Yuki Tokushige (TUM):
TBA(using zoom) (Parkring 11, 85748 Garching-Hochbrück)

TBA

31.05.2021 15:00 Montie Avery (University of Minnesota):
TBAVirtuelle Veranstaltung (Boltzmannstr. 3, 85748 Garching)

tba