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I'm Jacob! I'm a postdoc at the University of Copenhagen, currently affiliated with the Center for Information and Bubble Studies (CIBS). My research is focused towards statistical models of network dynamics, such as social networks and neurons.


About me

I hold a PhD in Statistics (2018) from the University of Copenhagen and a Masters degree in statistics (2010) from Aarhus University. Between my degrees I worked 4 years as a quant in risk modeling. My main research interest is analysis of networks, see more below.

I was raised in the countryside in the southern part of Denmark and enjoy the outdoors, especially sailing. I can also occasionally be caught playing music, reading a Steinbeck novel, watching a classic noir such as the Maltese Falcon or simply just floating around the internet learning all sorts of peculiar historical facts and skills.

I have a curious mind and therefore often engage myself in new challenges.

My research

My research deals with analysis of networks and how to encode dynamics of observed point processes. I aim to construct statistical tools that can aid in exploring how networks are connected, such as social networks and neural networks. These can help researchers explain phenomena that occurs in mass communication dynamics as well as physiological processes in the brain. The encoding of dynamical trends and fingerprints of processes are useful to adress questions on important factors that influence the observed process. For instance, stimulating neurons or intervening in a communication channel will change the behavior of these processes. 

Papers and preprint drafts are available below.

Keywords: multivariate stochastic differential equations, network analysis, cointegration, Bayesian filtering, point processes.


Papers and software

Frederiksberg Beamer Theme


A LaTeX Beamer theme styled as the official UCPH design

UCPH Colors


R package with the official UCPH colors.

Oscillating systems with cointegrated phase processes

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Paper on network inference in oscillating neural processes by applying cointegration analysis. A model is derived and showcased to infer the network connectivity, including directed interaction.

Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models

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Paper on how Generalized Linear Models capture variability of simulated Izhikevich neurons. The paper describes how to assess goodness-of-fit of this type of model along with a discussion of the application to neural data.

A state space model for bursting neurons

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Working paper on a state space model where the latent component controls the bursting behavior of a single neuron.

High-dimensional cointegration

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Working paper on high-dimensional cointegration applied to a linearized Kuramoto toy model. The paper explores testing for the rank in high dimensions and structural restrictions for the estimation of the system matrix.

Statistical Methods for Neural Data:
Cointegration Analysis of Coupled Neurons & Generalized Linear Models for Spike Train Data

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My PhD Thesis on models for both network analysis and single neuron dynamics. Includes the 4 papers above.

Jacob Stærk-Østergaard

University of Copenhagen
Karen Blixens Vej 4
DK-2300 Copenhagen S

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