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.
Location
Aarhus University
Auditorium D4, 116 Ny Munkegade, Aarhus C, 8000
Authors
Quantitative Researcher
Quantitative Researcher with a PhD in Mathematics, specializing in stochastic modeling, machine learning, and predictive systems for financial markets.
Experienced in probabilistic modeling, Monte Carlo simulation, uncertainty quantification, and statistical validation for data-driven decision-making.
Currently developing intraday energy-market price prediction models and optimal liquidation strategies using machine learning, functional data analysis, and stochastic differential equations.
Interested in market prediction problems where model quality is directly reflected in trading performance and PnL.