Talk - Topological Data Analysis Based Models of Evolving Higher-Order Networks
Jun 17, 2024
·
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
Event
Location
Aarhus University
Auditorium D4, 116 Ny Munkegade, Aarhus C, 8000
Authors
Quantitative Researcher
Quantitative researcher with a PhD in Mathematics, combining expertise in stochastic modeling, statistical inference, and machine learning with industrial experience in developing predictive systems.
Experienced in large-scale time-series analysis, uncertainty quantification, and rigorous model validation, with strong Python/C++ skills and a proven track record of translating research into production software.
Currently developing intraday electricity-price models and stochastic-control methods for optimal liquidation at Aarhus University, in collaboration with an energy trading firm.