• Algorithm design and theory
• Machine learning
• Computational statistics
• Large and high-dimensional data
• Data streams

Selected publications

Alexander Munteanu, Simon Omlor, and David P. Woodruff (2021). Oblivious sketching for logistic regression. In Proceedings of the 38th International Conference on Machine Learning (ICML), pages 7861–7871.
http://proceedings.mlr.press/v139/munteanu21a.html

Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, and Christian Sohler (2020). Streaming statistical models via Merge & Reduce. International Journal of Data Science and Analytics, 10(4):331–347.
https://doi.org/10.1007/s41060-020-00226-0

Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, and David P. Woodruff (2018). On coresets for logistic regression. In Advances in Neural Information Processing Systems 31 (NeurIPS), pages 6562–6571.
https://proceedings.neurips.cc/paper/2018/hash/63bfd6e8f26d1d3537f4c5038264ef36-Abstract.html

Alexander Munteanu and Chris Schwiegelshohn (2018). Coresets - Methods and History: A theoreticians design pattern for approximation and streaming algorithms. Künstliche Intelligenz, 32(1):37–53.
https://doi.org/10.1007/s13218-017-0519-3

Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, Jens Quedenfeld, and Christian Sohler (2017). Random projections for Bayesian regression. Statistics and Computing, 27(1):79–101.
https://doi.org/10.1007/s11222-015-9608-z