Research Interests
To specialize on a very
specific scientific area is the common approach for a young scientist.
However, this potentially misses great opportunities. During my PhD I
discovered that two different mathematical communities had been working on the
same problem for more than a decade without knowing of each other. I thus
believe that it is necessary for different scientific communities to talk to
each other, and to understand the others' research and goals. My general aim
as a scientist is to help bridging the gap between different fields of
science.
The following descriptions contain a few selected publications each. My full list of publications can be found here, and a more detailed CV here.
During my PhD at University of Saarland with Prof. ErnstUlrich
Gekeler I worked in the intersection between number theory, algebra and
probability theory:
I have since then turned to several more applied areas, including
computer science and neuroscience. My greatest challenge so far was to gather
enough biological background to contribute to the neuroscience
community:

A hippocampal model for behavioral time acquisition and fast bidirectional replay of spatiotemporal memory sequences
(joint work with Marcelo Matheus Gauy, Hafsteinn Einarsson, Florian Meier, Felix Weissenberger, Mehmet Fatih Yanik, Angelika Steger)
Frontiers in neuroscience, 2019

Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuli
(joint work with Felix Weissenberger, Marcelo Matheus Gauy, Florian Meier, Angelika Steger)
Nature Scientific Reports, 2018

Long synfire chains emerge by spiketiming dependent plasticity modulated by population activity (preprint)
(joint work with Felix Weissenberger, Florian Meier, Hafsteinn Einarsson, Angelika Steger)
International Journal of Neural Systems, 2017

A Model of Fast Hebbian Spike Latency Normalization
(joint work with Hafsteinn Einarsson, Marcelo Matheus Gauy, Angelika Steger)
Frontiers in Computational Neuroscience, 2017

Note on the coefficient of variations of neuronal spike trains
(joint work with Angelika Steger)
Biological Cybernetics, 2017

Randomness as a Building Block for Reproducibility
in Local Cortical Networks
Book chapter in Reproducibility: Principles, Practices, Problems, eds. H. Atmanspacher and S. Maasen, Wiley, New York, 2016

A highcapacity model for one shot association learning in the brain
(joint work with Hafsteinn Einarsson, Angelika Steger)
Frontiers in Computational Neuroscience, 2014.

Reliable neuronal systems: the importance of heterogeneity
(joint work with Florian Jug, Angelika Steger)
PLoS ONE, 2013.
I also enjoy working on evolutionary (bioinspired) algorithms for discrete optimzation. The following is a small sample of my work there:

Drift Analysis
Book chapter in Theory of Evolutionary Computation, eds. B. Doerr and F. Neumann, Springer, Cham, 2020

A General Dichotomy of Evolutionary Algorithms on Monotone Functions
IEEE Transactions on Evolutionary Computation, 2020

Drift Analysis and Evolutionary Algorithms Revisited
(joint work with Angelika Steger)
Combinatorics, Probability, and Computing, 2018

The complex parameter landscape of the compact genetic algorithm
(joint work with Dirk Sudholt, Carsten Witt)
Algorithmica, 2021

The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates
(joint work with Hafsteinn Einarsson, Marcelo Matheus Gauy, Florian Meier, Asier Mujika, Angelika Steger, Felix Weissenberger)
Genetic and Evolutionary Computation Conference (GECCO '18)

The (1+1) Elitist BlackBox Complexity of LeadingOnes
(joint work with Carola Doerr)
Algorithmica, 2017

Introducing Elitist BlackBox Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?
(joint work with Carola Doerr)
Journal of Evolutionary Computation, 2016

OneMax in BlackBox Models with Several Restrictions
(joint work with Carola Doerr)
Algorithmica, 2016

BlackBox
Complexities of Combinatorial Problems
(joint work with Benjamin Doerr, Timo Kötzing, Carola
Winzen)
Theoretical Computer Science, 2013
In the last years I started working on random graph models for large realworld networks. I am proud that our model of Geometric Inhomogeneous Random Graphs (GIRGs) is now established and increasingly picked up by other authors and communities. An introduction into this fascianting topic can be found in the script for my course Complex Network Models. Central papers include:

Geometric Inhomogeneous Random Graphs
(joint work with Karl Bringmann, Ralph Keusch)
Theoretical Computer Science, 2019.

Greedy Routing and the Algorithmic SmallWorld Phenomenom
(joint work with Karl Bringmann, Ralph Keusch, Yannic Maus, Anisur Molla)
Journal of computer and system sciences2022.

Penalising transmission to hubs in scalefree spatial random graphs
(joint work with Julia Komjathy, John Lapinskas)
Annales de l'Institut Henri Poincare, 2021.

Bootstrap percolation on geometric inhomogeneous random graphs
(joint work with Christoph Koch)
Internet Mathematics, 2021.

Normalization Phenomena in Asynchronous Networks
(joint work with Amin Karbasi, Angelika Steger)
Proc. of International Colloquium on Automata, Languages, and Programming (ICALP 2015), 2015
I have also worked on
various other topics in computer science and mathematics, for example:

Random Sampling with Removal
(joint work with Kenneth Clarkson, Bernd Gärtner, May Szedlak
Discrete and Computational Geometry, 2020

Bootstrap percolation with inhibition
(joint work with Hafsteinn Einarsson, Frank Mousset, Konstantinos Panagiotou, Angelika Steger)
Random Structures and Algorithms, 2019

Asymptotically optimal amplifiers for the moran process
(joint work with Leslie Goldberg, John Lapinskas, Florian Meier, Konstantinos Panagiotou, Pascal Pfister
Theoretical Computer Science, 2019

Tight Analysis for the 3Majority Consensus Dynamics
(joint work with Mohsen Ghaffari)
Principles of Distributed Computing (PODC 2018)

Connectivity Thresholds for Bounded Size Rules
(joint work with Hafsteinn Einarsson, Frank Mousset, Konstantinos Panagiotou, Angelika Steger)
Annals of Applied Probability, 2016