Random connection hypergraphs
In this paper, we introduce a novel model for random hypergraphs based on weighted random connection models. In accordance to the standard theory for hypergraphs, this model is …
morten-brun
Summary
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.
Interests
Stochastic Modeling
Forecasting
Algorithm Design
Python & Data Analytics
Education
PhD in Mathematics
2022-11-01
2025-10-31
Aarhus University
MBA in Finance
2019-09-01
2021-06-30
Budapest University of Technology and Economics
MSc in Physics
2013-09-01
2016-06-30
Budapest University of Technology and Economics
BSc in Physics
2010-09-01
2013-06-30
Budapest University of Technology and Economics
Skills
Modeling
Stochastic Modeling
Probability
Data Analysis
Programming
Python
C++
Git
SQL
Languages
100%
English
Fluent
40%
German
Beginner
100%
Hungarian
NativeExperience
Research Assistant in Quantitative Finance
Aarhus University
- Developed predictive models for limit order book mid-price movements using high-frequency trading data from the 2019 XTX Market Prediction Challenge.
- Explored feature engineering approaches for market microstructure data, including order imbalance and short-term price dynamics.
- Studied Bayesian uncertainty estimation techniques for probabilistic machine learning and calibrated prediction intervals. Applied Python-based ML workflows for large-scale time-series modeling and evaluation.
Researcher in Probability
Aarhus University
- Developed stochastic models to analyze and predict structural and statistical properties of dynamic real-world systems.
- Designed statistical validation methods to rigorously evaluate model assumptions and predictions against empirical observations.
- Built large-scale Monte Carlo simulations and algorithms in Python and C++ to stress-test models, evaluate edge cases, and quantify uncertainty.
- Presented research at international conferences in probability, stochastic modeling, and data analysis.
- Taught advanced probability, data analysis, and differential equations to mathematics and data-science students.
Machine Learning Researcher in Automated Driving
Bosch Group Hungary
- Patented a Gaussian regression-based trajectory prediction model, improving predictive accuracy by 3x and uncertainty calibration by 10x over production baselines.
- Built stochastic forecasting and uncertainty quantification models in Python for safety-critical, real-time decision-making in automated driving.
- Translated Python prototypes into scalable C++ systems with explicit reliability criteria.
- Designed and executed large-scale data analyses and validation experiments to evaluate model assumptions and predictions against observed outcomes.
- Led a team of 11 developing, validating, and integrating environment prediction models for automated driving as a technical lead.
- Oversaw 2 teams in the integration of driver monitoring systems.
Software Engineer for Mobile Networks
Ericsson Telecommunications Hungary
- Optimized load-balancing strategies by analyzing response times of distributed systems.
- Implemented performance-critical algorithms for mobile networks.
- Coordinated a team of 8 as a deputy of the product owner.
Publications
•
•
1 min read
PhD Thesis: Stochastic Models of Higher-Order Networks - Point Processes and Topological Data Analysis
Higher-order networks model interactions among groups of entities of an arbitrary number. This thesis introduces stochastic models grounded in point process theory that are capable …
peter-juhasz
Functional limit theorems for edge counts in dynamic random connection hypergraphs
We introduce a dynamic random hypergraph model constructed from a bipartite graph. In this model, both vertex sets of the bipartite graph are generated by marked Poisson point …
christian-hirsch
On the topology of age-dependent random connection network models
In this paper, we investigate the potential of the age-dependent random connection model (ADRCM) with the aim of representing higher-order networks. A key contribution of our work …
christian-hirsch
Poisson approximation of fixed-degree nodes in weighted random connection models
We present a process-level Poisson-approximation result for the degree-k vertices in a high-density weighted random connection model with preferential-attachment kernel in a …
christian-hirsch
Topicality boosts popularity: A comparative analysis of NYT articles and Reddit memes
This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping …
kate-barnes
Information propagation in stochastic networks
In this paper, a network-based stochastic information propagation model is developed. The information flow is modeled by a probabilistic differential equation system. The numerical …
peter-juhasz
A Bayesian approach to identify Bitcoin users
Bitcoin is a digital currency and electronic payment system operating over a peer-to-peer network on the Internet. One of its most important properties is the high level of …
peter-juhasz
Finite element method plasma simulation of nitrogen contaminated metal halide lamps
The ceramic metal halide lamps belong to the most efficient high intensity light sources of these days. Presently it is the nitrogen contamination that causes the most significant …
peter-juhasz
Patents
Method and apparatus for processing data associated with at least one ultrasonic sensor
A method for processing data associated with at least one ultrasonic sensor. The method includes receiving at least one signal characterizing at least a portion of a transmitted …
georg-rempfer
Method and device for predicting the trajectory of a traffic participant, and sensor system
A computer-implemented method for predicting a trajectory of a traffic participant. Sensor data acquired by at least one vehicle sensor at a plurality of acquisition times is …
peter-juhasz
Awards, Grants
Individual Travel Grant for Presenting a Talk at GPSD 2025
European Mathematical Society ∙
March 2025
500 EUR Random Connection Hypergraphs
Academic Mobility Grant for International Exchange
Aarhus University ∙
October 2024
20 000 DKK Evolving Stochastic Network Models
PhD Fellowship Grant – DDSA-PhD-2022-008
Danish Data Science Academy ∙
November 2022
1 890 000 DKK Stochastic Models of Higher-Order Networks
Excellence Award for Outstanding Thesis and Diploma Work
Hungarian National Bank ∙
September 2020
350 000 HUF Digitalization, Artificial Intelligence, and the Age of Data – What Makes a Meme Viral?
Erasmus+ Scholarship
Technische Universiteit Delft ∙
September 2015
3000 EUR Erasmus+ Scholarship for Academic Excellence
Additional Qualifications
(2021).
Certified Software Designer.
– Bosch Group.
(2021).
Certified Professional for Software Architecture.
– ISAQB.
(2020).
Social and Economic Networks: Models and Analysis.
– Coursera.
Events
Teaching
Miscellaneous
Contact
Visit Us
118 Ny Munkegade
Aarhus C, Central Jutland
8000 Denmark DK