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Non-Convex Optimization in Networks and High-Dimensional Covariance Estimation
(2024)In this thesis, we consider two sub-fields of Mathematics, on the one hand Mathematics of Data Science, on the other hand High-Dimensional Probability. The first part of this work is dedicated to problems in non-convex optimization arising in inverse problems on networks and synchronization in complex networks. The second part is dedicated to robust estimation of covariance matrices of high-dimensional vectors, mainly in the context of ...Doctoral Thesis -
THE BIOGEOGRAPHY OF KEY FUNCTIONAL TRAITS AND COMMUNITY STRUCTURES ACROSS GLOBAL FORESTS
(2024)Doctoral Thesis -
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Rayleigh-Taylor turbulence with mean shear and interface tension
(2024)Rayleigh-Taylor (RT) turbulence originates at the interface between two fluid layers in the presence of an unstable density stratification \citep{rayleigh1882investigation,taylor1950instability,fermi1953taylor}. In nature, RT turbulence is usually influenced by additional factors. Firstly, the two fluid layers may move at different velocities in the horizontal direction so that a mean shear compounds the unstable density profile. Secondly, ...Doctoral Thesis -
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Taming Reactive Nitrogen Intermediates for Amination and Deaminative Coupling Reactions
(2024)Doctoral Thesis -
Taming Reactive Nitrogen Intermediates for Amination and Deaminative Coupling Reactions
(2024)Doctoral Thesis -
Nervous Ecologies: Late Modernist Architecture, the Whole Earth Network, and the Postwar Overpopulation Discourse.
(2024)By the late 1960s and early 1970s, pundits were increasingly interpreting superficially unrelated phenomena, such as pollution, the deterioration of nature, rundown inner cities, sprawling suburbanization, brittle infrastructure, escalating crime, uprisings of racial minorities, and student unrest in the United States—as well as hunger, poverty, and Communist insurgencies in the Global South—as dissimilar expressions of one underlying ...Doctoral Thesis -
Causality in Unsupervised Learning: Methods and Applications in Cancer Genomics
(2024)Unsupervised learning deciphers the beautifully complex patterns embedded within vast amounts of data. As one of the main branches of machine learning, it seeks to discover hidden patterns in unlabelled data. One of the prevalent techniques within unsupervised learning is clustering, which groups data into distinct subsets of shared characteristics. In cancer research, clustering offers promising avenues to stratify patients based on their ...Doctoral Thesis -