In the following, I present the projects i am/was either leading or working/worked in as a member. During my life as a PhD, I have already gained experience in BMBF, 2020 Horizon, DFG projects etc. In particular I have gained experience in requesting funding or helping to write proposals such as BMBF, DFG, ERC 2020 Horizon projects.

Adversarial Design Framework for Self-Driving Networks (ADVISE)

I am the Full PI of this DFG/FWF-funded project on the German side. I was fully responsible for writing and requesting funding for this project. I finally received funding for a full PhD position for 3 years.


Inspired by self-driving cars, the networking community is currently engaged in designing more automated and ‘‘self-driving’’ communication systems, aiming to overcome the cumbersome and error-prone manual approach to manage and operate networks. Ideally, such self-driving networks also allow to exploit the increasing flexibilities introduced by emerging new Internet technologies, such as software-defined and virtualized communication technologies. With these technologies, the networks allow to meet the stringent performance requirements of new networks (e.g., 5G and 6G) and workloads (e.g., low-latency tele-operation or high-bandwidth machine-to-machine type communication), by adapting to the context and demand.

The Internet, one of the largest and most complex artefacts built by mankind, has evolved organically over the last decades, and many design choices were taken based on experience and best practices. This project proposes a novel network framework to design and operate such networks, relying on the vision of such self-driving networks, and studying how to integrate Machine Learning and Artificial Intelligence concepts into existing networks. In order to overcome the potential concerns regarding the dependability of such Artificial Intelligence and Machine Learning approaches, we envision a hybrid solution which keeps the human in the loop. Hence, we first ask three fundamental questions in this project: how predictable are today’s networks, i.e., user demands, workload traffic, and behavior of network functions? Can we make network design and algorithms data-driven and human interpretable? How to design a network framework that combines both, generative workload models and data-driven algorithms with guarantees?

The novelty of this project lies in the integration and application of Artificial Intelligence and Machine Learning on designing network algorithms. For the first time, Artificial Intelligence and Machine Learning should be integrated also in the testing and the developing phase of new networking solutions, and not only solely applied to solving problems. In terms of methodologies, we consider adversarial and game-theoretic approaches to test and optimize networks, to leverage the performance benefits from Machine Learning approaches while at the same time provide rigorous worst-case guarantees. Finally, a proof-of-concept implementation should demonstrate the new framework.


01/01/2021 - 12/31/2023


Funding Agency


I was an active team member contributing research papers. Furthermore, I was responsible for reporting and partially managing financials.

Link: LKN Webpage

Funding Agency

BMBF - Bundesministerium für Bildung und Forschung

ERC Consolidator Grant: “Quantifying Flexibility in Communication Networks (FlexNets)”

I am an active team member and helped in writing the proposal.

Link: LKN Webpage

Funding Agency

European Research Council (ERC)

Anwendungsorientierte, SDN-basierte Steuerungsarchitekturen für Kommunikationsnetze und ihre Leistungsbewertung

I was an active team member and was mainly responsible in writing the research proposal for the TUM part.

Link: LKN Webpage

Funding Agency

DFG - Deutsche Forschungsgemeinschaft

ZIM Projekt: Configuration Management in SDN

This was a joint research project together with Infosim I was an active team member in this project.

Funding Agency

BMWi - Bundesministerium für Wirtschaft und Energie