Talk - Stochastic Models of Higher-Order Networks -- Point Processes and Topological Data Analysis
Dec 15, 2025
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0 min read
Abstract
During his PhD studies, Péter Juhász researched stochastic network models that describe how complex systems emerge from random interactions. Such models are used to capture the structure of communication systems, biological networks, and collaborations, where randomness and group interactions play a key role. Péter Juhász focused on how fundamental network characteristics, such as node degrees and connection patterns, scale in large systems and how interacting groups of nodes beyond simple pairs shape the overall network. The research provides new mathematical insights into how novel stochastic network models influence both local and global structures of complex systems, advancing their understanding.
Date
Dec 15, 2025 2:15 PM — 4:30 PM
Event
Location
Aarhus University
Auditorium D3, 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.