- My colleague Pascal Welke and I supervised two seminars on learning theory in the last two semesters in which the students studied the book “Understanding Machine Learning: from Theory to Algorithms” by Shai Shalev-Shwartz and Shai Ben-David. This resulted in two great summaries, one on the theoretical part, and one on the algorithmic part. Really great work by all the students!
- I am co-chair of the 2nd International Workshop on Decentralized Machine Learning at the Edge (DMLE’19) at this years ECML PKDD’19 (A, top 18%). We are looking forward to your contributions (CfP).
- I am co-chair of the 1st International Workshop on Machine Learning for Cybersecurity (MLCS’19) at this years ECML PKDD’19 (A, top 18%). We are looking forward to your contributions.
- I am co-chair of the 1st International Workshop on Data-Centric Dependability and Security (DCDS’19) at this years DSN (A, top 18%). We are looking forward to your contributions (CfP).
- We presented a paper about Corresponding Projections for Orphan Screening at the ML4H workshop at NeurIPS’18 (A*, top 4%), a pre-print of the paper is online.
- Our paper on Efficient Decentralized Deep Learning by Dynamic Model Averaging got accepted at ECML PKDD’18 (A, top 18%), a pre-print of the paper is online (joint work with colleagues from Volkswagen Group Research).
- I am co-chair of the workshop on Decentralized Machine Learning at the Edge (DMLE’18) at this years ECML PKDD the largest European conference on Machine Learning and Data Mining (A, top 18%).
- We presented a paper on Effective Parallelisation for Machine Learning at NIPS’17 (A*, top 4%), the print version and a teaser video are online (joint work with colleagues and friends from the University of Nottingham, Max Planck Institute for Informatics, and GOOGLE).
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 research fellow at Monash University. From 2010 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.