Faculty
Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)
I am a tenure-track faculty at the Helmholtz Center for Information Security (CISPA) in Saarbrücken. The focus of my research is relational machine learning. My main goal is to gain a theoretical understanding of deep learning from a complex network perspective and improve contemporary algorithms based on these insights. My favourite applications are currently in molecular biology.
From 2019-2021, I was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health working with John Quackenbush. Before that, I enjoyed postdoctoral research at ETH Zurich, from 2017-2018 at the Institute for Machine Learning with Joachim Buhmann and from 2016-2017 at the Chair of Systems Design with Frank Schweitzer. My PhD research from 2013-2016 at the ETH Risk Center was supervised by Frank Schweitzer and co-supervised by Hans J. Herrmann. My thesis on systemic risk won the Zurich Dissertation Prize and my work on international maize trade received the CSF Best Contribution Award. I studied Mathematics and Physics at TU Darmstadt.
Link to my personal homepage:
Genome Biology
Nucleic Acids Research
NeurIPS
Thirty-sixth Conference on Neural Information Processing SystemsNeurIPS 2022
ICML
Proceedings of the 39th International Conference on Machine LearningInternational Conference on Machine Learning (ICML)
ICLR
International Conference on Learning RepresentationsThe Tenth International Conference on Learning Representations
ICLR
International Conference on Learning RepresentationsThe Tenth International Conference on Learning Representations
Nature Methods
Pruning deep neural networks for lottery tickets
Deep learning has achieved major breakthroughs in a variety of tasks. Yet, it comes at a considerable computational cost, which is exaggerated by the recent trend towards ever wider and deeper neural network architectures. Instead, many problems can be solved with the help of extremely sparse neural network architectures but finding and training them is a non-trivial task. According to the recent lottery ticket hypothesis, such sparse architectures can be identified by pruning large randomly initialized neural networks. In this seminar, we will present recent algorithmic advancements in this direction, gain theoretical insights into the existence of lottery tickets, identify open problems, and discuss common challenges in the quest for winning lottery tickets.
In this seminar, students will learn to present, discuss, and summarize papers related to the lottery ticket hypothesis. Specifically, each student will get a single topic assigned to them, consisting of two papers (a lead and follow-up paper). Each student will
In addition to your review you will have to submit 3 questions that you will ask the presenter of the paper.
Presentation: (40%). You will prepare and deliver a 30 min presentation (followed by 15 mins question/discussion) of the paper assigned to you. You will have the possibility to get feedback on your slides before the presentation.