Looking for PhDs
I am offering two full PhD positions. If you are interested, please send me an email.
- My colleagues Henning Petzka, Linara Adilova, Cristian Sminchisescu, Mario Boley and I published the paper “Relative Flatness and Generalization” at NeurIPS 2021 (A*, top 7%).
- I received the NeurIPS 2021 outstanding reviewer award.
- My colleagues Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Qi Dou, and I published the paper “FedBN: Federated Learning on Non-IID Features via Local Batch Normalization” at ICLR 2021 (A*, top 7%).
- I received a 2021 ICLR reviewer award.
- I was invited as keynote speaker at the Workshop on Data-Centric Dependability and Security (DCDS) where I gave a presentation on Secure and Trustworthy Federated Learning.
- I am co-chair of the Workshop on Parallel, Distributed, and Federated Learning (PDFL’21) at ECMLPKDD 2021 (A, top 18%).
- I am co-chair of the International Workshop on Machine Learning for Cybersecurity (MLCS’21) at ECMLPKDD 2021 (A, top 16%). We are looking forward to your contributions (CfP).
- I am honored to be invited to serve as an editorial board member for the Springer journal Machine Learning.
- Our paper “Resource-Constrained On-Device Learning by Dynamic Averaging” won the Best Paper Award at the PDFL’20 workshop. Congratulations to Lukas, and our co-authors.
- My colleagues Pascal Welke, Florian Seiffarth, Stefan Wrobel and I published the paper “hoPS: Probabilistic Subtree Mining for Small and Large Graphs” in SIGKDD 2020 (A*, top 4%).
- I received the NeurIPS 2019 and ICML 2019 best reviewers award.
My main research interests are efficient parallelizations for machine learning and data mining algorithms. Many of today’s parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, the volumes of data generated from machine-to-machine interaction, by mobile phones or autonomous vehicles, surpass the amount of data that can be realistically centralized. Thus, traditional cloud computing approaches are rendered infeasible. To scale parallel machine learning to such volumes of data, computation needs to be pushed towards the data generating devices. An efficient parallelization is able to scale a machine learning algorithm – or better a class of algorithms – to large numbers of parallel instances, thereby achieving a substantial speed-up. At the same time, the resulting model has a similar quality than a hypothetical centrally computed one. I’m interested both in parallelizations for classical machine learning algorithms from batch data, as well as online learning / optimization algorithms. The latter algorithms are especially suited for distributed / decentralized learning from data streams. The approaches I’m seeking to parallelize are often based on linear models or kernel methods, as well as decentralized deep learning. I am also working on the theoretical foundations of deep learning, interpretability, informed machine learning, and multi-view, semi-supervised machine learning. Application areas which I am often considering when looking for novel machine learning challenges include autonomous driving, cybersecurity, real-time services, financial analysis, and chemoinformatics.
Curriculum Vitae – Highlights
I am a postdoctoral researcher at the CISPA Helmholtz Center for Information Security in the Exploratory Data Analysis group of Jilles Vreeken. From 2019 to 2021 I was a postdoctoral research fellow at Monash University, where I am still an affiliated researcher. From 2011 to 2019 I was a data scientist at Fraunhofer IAIS, where I lead Fraunhofer’s part in the EU project DiSIEM, managing a small research team. Moreover, I was a project-specific consultant and researcher, e.g., for Volkswagen, DHL, and Hussel, and I designed and gave industrial trainings. Since 2014 I was simultaneously a doctoral researcher at the University of Bonn, teaching graduate labs and seminars, and supervising Master’s and Bachelor’s theses. Before that, I worked for 10 years as a software developer.