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Dr. Alexander Munteanu

Mathematische Statistik und biometrische Anwendungen


Mathematics Building,
Room E16b
+49 231 755 - 7885
+49 231 755 - 5303
Fakultät Statistik
Technische Universität Dortmund
44221 Dortmund

About me

After receiving my PhD in theoretical computer science under supervision of Christian Sohler, I am now postdoctoral researcher at the Statistics department of TU Dortmund in the group led by Katja Ickstadt.

I am principle investigator in a joint project on large-scale and high-dimensional regression problems within the collaborative research center SFB 876 and I am happy to advise Simon Omlor as my first PhD student.

Being a PI in the interdisciplinary research area From Prediction to Agile Interventions in the Social Sciences (FAIR) offers me a great opportunity to transfer and further develop innovative statistical and data science methods for their application in the social sciences.

Last but not least, I am happy to help building up the Dortmund Data Science Center in the position of its managing director.

Research interests

I am mainly interested in the design and analysis of algorithms for tackling the challenges of massive data and high dimensionality. I am also interested in collaborating on possible applications. My research involves several scientific fields such as

  • streaming and distributed algorithms,
  • randomized linear algebra,
  • machine learning,
  • computational statistics,
  • computational geometry,
  • convex optimization.
  • Publications


    • Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff.
      Bounding the width of neural networks via coupled initialization - A worst case analysis.
      International Conference on Machine Learning (ICML), 2022.
    • Alexander Munteanu, Simon Omlor, Christian Peters.
      p-Generalized probit regression and scalable maximum likelihood estimation via sketching and coresets.
      International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.


    • Alexander Munteanu, Simon Omlor, David P. Woodruff.
      Oblivious sketching for logistic regression.
      International Conference on Machine Learning (ICML), 2021.


    • Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, Christian Sohler.
      Streaming statistical models via Merge & Reduce.
      International Journal of Data Science and Analytics, 10(4):331-347, 2020.


    • Stefan Meintrup, Alexander Munteanu, Dennis Rohde.
      Random projections and sampling algorithms for clustering of high-dimensional polygonal curves.
      Advances in Neural Information Processing Systems (NeurIPS), 2019.
    • Alexander Munteanu, Amin Nayebi, Matthias Poloczek.
      A framework for Bayesian optimization in embedded subspaces.
      International Conference on Machine Learning (ICML), 2019.
    • Amer Krivosija, Alexander Munteanu.
      Probabilistic smallest enclosing ball in high dimensions via subgradient sampling.
      Symposium on Computational Geometry (SoCG), 2019.
      European Workshop on Computational Geometry (EuroCG), 2019.


    • Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff.
      On coresets for logistic regression.
      Advances in Neural Information Processing Systems (NeurIPS), 2018.
    • Alexander Munteanu.
      On large-scale probabilistic and statistical data analysis.
      PhD Thesis. TU Dortmund University, 2018.
    • Kristian Kersting, Alejandro Molina, Alexander Munteanu.
      Core dependency networks.
      AAAI Conference on Artificial Intelligence (AAAI), 2018.
    • Alexander Munteanu, Chris Schwiegelshohn.
      Coresets - methods and history: a theoreticians design pattern for approximation and streaming algorithms.
      KI special issue on "Algorithmic Challenges and Opportunities of Big Data", 32(1):37-53, 2018.


    • Leo N. Geppert, Katja Ickstadt, Alexander Munteanu, Jens Quedenfeld, Christian Sohler.
      Random projections for Bayesian regression.
      Statistics and Computing, 27(1):79-101, 2017.


    • Alexander Munteanu, Max Wornowizki.
      Correcting statistical models via empirical distribution functions.
      Computational Statistics, 31(2):465-495, 2016.


    • Dan Feldman, Alexander Munteanu, Christian Sohler.
      Smallest enclosing ball for probabilistic data.
      Symposium on Computational Geometry (SoCG), 2014.
    • Marc Heinrich, Alexander Munteanu, Christian Sohler.
      Asymptotically exact streaming algorithms.
      ArXiv preprint, CoRR abs/1408.1847, 2014.


    My interdisciplinary teaching activities are listed below. Students interested in Bachelor's or Master's theses in either Computer Science, Statistics, or Data Science may contact me anytime via email. Office hours only by appointment.