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Personal profile

Research interests

I work on theoretically sound and practically useful machine learning. In particular, I aim to scale machine learning through parallelization. Since the amount of data-generating devices is growing rapidly, traditional cloud-based approaches to learning from their data become infeasible. Instead, pushing the learning onto - or close to - the devices allows to scale the learning, effectively use the device's computing power, minimize communication-overhead, and to protect privacy-sensitive data. For such parallelizations I seek theoretical guarantees on model quality, speedup, communication overhead, and privacy; at the same time I strive to provide practically useful software and tools.

I also work on the theoretical foundations of deep learning, seeking to understand the connections between the training process and the generalization abilities of neural networks. This is important not only for sound parallelizations, but in a broader sense for the interpretability and trustworthiness of deep learning.

Application domains I have worked on include machine learning for the automotive industry (e.g., autonomous driving), cybersecurity, biochemistry, and financial time-series.

Research area keywords

  • Machine Learning
  • distributed optimisation
  • Parallel Computing
  • statistical learning theory
  • Deep Learning
  • Deep Learning for Cyber Security
  • co-regularization
  • Convex Optimization
  • Online Learning

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2013 2019

First international workshop on data-centric dependability and security (DCDS)

Medeiros, I., Gashi, I., Kamp, M. & Ferreira, P., 2019, Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2019, Workshop Volume. Roy, M. & Huang, Y. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. IX 1 p. 8806016

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Open Access

System misuse detection via informed behavior clustering and modeling

Adilova, L., Natious, L., Chen, S., Thonnard, O. & Kamp, M., 2019, Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019, Workshop Volume. Roy, M. & Huang, Y. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 15-23 9 p. 8806013

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Co-Regularised Support Vector Regression

Ullrich, K., Kamp, M., Gärtner, T., Vogt, M. & Wrobel, S., 2017, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18–22, 2017 Proceedings, Part II. Ceci, M., Hollmen, J., Todorovski, L., Vens, C. & Dzeroski, S. (eds.). Cham Switzerland: Springer, p. 338-354 17 p. (Lecture Notes in Computer Science; vol. 10535 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Effective parallelisation for machine learning

Kamp, M., Boley, M., Missura, O. & Gärtner, T., 2017, NIPS Proceedings: Advances in Neural Information Processing Systems (NIPS 2017). Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R. & Vishwanathan, S. (eds.). San Deigo CA USA: Neural Information Processing Systems Foundation Inc., 22 p. (Advances in Neural Information Processing Systems; no. 30).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

34 Citations (Scopus)

Issues in complex event processing: status and prospects in the Big Data era

Flouris, I., Giatrakos, N., Deligiannakis, A., Garofalakis, M., Kamp, M. & Mock, M., May 2017, In : Journal of Systems and Software. 127, p. 217-236 20 p.

Research output: Contribution to journalArticleResearchpeer-review