Dr. Alexander Munteanu

Contact data
Address Dekanat Statistik - Dortmund Data Science Center
Technische Universität Dortmund
Vogelpothsweg 87
44227 Dortmund
Office Campus Nord, Mathematics Building, Room E16b
Phone +49 231 755-7885
Fax +49 231 755-5303
Email alexander.munteanu(at)tu-dortmund.de

About me: After receiving my PhD in theoretical computer science under supervision of Christian Sohler, I am now postdoctoral researcher in the statistics department of TU Dortmund in the group led by Katja Ickstadt.
I was PI in a project on large-scale and high-dimensional regression problems within the large-scale collaborative research center SFB 876 (completed 2022).
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.
I am involved in the establishment of the TU Dortmund - Center for Data Science & Simulationas a founding member in the position of managing director.
I am happy to advise my students Simon Omlor (postdoc) and Amer Krivošija (postdoc).

Research Interests: I am mainly interested in the design and analysis of algorithms for tackling the challenges of massive data and high dimensionality.
This involves several fields of research such as streaming and distributed algorithms, randomized linear algebra,
machine learning, computational statistics, computational geometry, convex optimization.
I am also interested in collaborating on possible applications.

Teaching: My interdisciplinary teaching activities can be found in the bottom of this page. Students interested in Bachelor's or Master's theses in either Computer Science, Statistics, or Data Science may contact me anytime via e-mail. Office hours only by appointment.



  • Alexander Munteanu, Simon Omlor, David P. Woodruff.
    Almost linear constant-factor sketching for ℓ₁ and logistic regression.
    International Conference on Learning Representations (ICLR), 2023.
  • Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff.
    Optimal sketching bounds for sparse linear regression.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
  • Alexander Munteanu.
    Coresets and sketches for regression problems on data streams and distributed data.
    Machine Learning under Resource Constraints, Volume 1 - Fundamentals, pp. 85-97, 2023.
  • Zeyu Ding, Katja Ickstadt, Alexander Munteanu.
    Bayesian analysis for dimensionality and complexity reduction.
    Machine Learning under Resource Constraints, Volume 3 - Applications, pp. 58-70, 2023.


  • 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 P. 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.