Talk - Topological Data Analysis Based Models of Evolving Higher-Order Networks
Jun 17, 2024
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0 min read
Abstract
The field of complex networks capturing pairwise interactions has seen significant advancements over the past decades. On the other hand, higher-order networks capable of describing more complex group interactions, have only recently gained substantial attention. To study these networks, sophisticated mathematical tools, such as stochastic simplicial complex models and topological data analysis (TDA), are required. Despite former research, higher-order network models and their connection with real-world datasets remain poorly understood. The goal of this research project is to develop stochastic simplicial models to describe the structure and dynamics of higher-order networks, with applications in understanding scientific collaborations and social networks.
Date
Jun 17, 2024 10:00 AM — 12:00 PM
Location
Aarhus University
Auditorium D4, 116 Ny Munkegade, Aarhus C, 8000
Authors
Quantitative Researcher
I am a PhD researcher in Mathematics with experience in stochastic modeling, probabilistic analysis, and large-scale simulation, supported by Python/C++ model development.
Previously, I worked as a machine learning researcher at Bosch, where I developed and validated predictive models with a focus on uncertainty estimation and data-driven decision-making.
I am interested in applying quantitative methods to forecasting and risk modeling in energy and financial markets.