About me

I am a Senior Researcher and Principal Investigator at the Department of Statistics, TU Dortmund University. I am also the Managing Director of the TU Dortmund - Center for Data Science & Simulation (DoDaS). During the 2024-2025 academic year, I was appointed as a W3 Substitute Professor of Efficient Algorithms at the University of Cologne. My research bridges mathematical statistics and computer science, focusing on algorithms for data reduction, sketching, and scalable machine learning.

Third-Party Funding & Grants

I am Principal Investigator in a new project on Accelerating Deep Learning for Real-Time Medical Applications. The project will focus on model and data compression for real-time deployment of complex neural network models on resource-constrained devices (Lamarr-Institute for Machine Learning & Artificial Intelligence, 2026-2029).

I am currently leading my individual research grant on Data and Dimensionality Reduction for Large Scale Statistical and Machine Learning Problems. This project advances randomized Sampling and Sketching techniques and extends them to complex statistical models (DFG, 2024-2027).

I was Principal Investigator in the interdisciplinary research area From Prediction to Agile Interventions in the Social Sciences (FAIR) which offered me a great opportunity to transfer and further develop innovative statistical and data science methods for their application in the social sciences and to find unexpected inspiration for new theoretical insights (MKW.NRW, 2022-2025).

I was Principal Investigator in a project on Large-Scale and High-Dimensional Regression Problems within the large-scale collaborative research center CRC 876. The project established the fundamentals in Subsampling and Sketching for Bayesian regression and Generalized Linear Models (DFG, 2019-2022).

Research Group & Supervision

I lead a dynamic research group of postdocs and doctoral students. This includes leading the research track of my postdoc Dr. Simon Omlor, as well as mentoring my former postdoc Dr. Amer Krivošija, who has transitioned to a career in industry. Furthermore, I successfully co-supervised Dr. Zeyu Ding, who graduated in February 2026, and I am excited to lead Tim Novak's upcoming PhD track.

Research interests

I am broadly 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

• Machine Learning Theory and Algorithms,
• Randomized Numerical Linear Algebra,
• Streaming and Distributed Algorithms,
• Mathematical Statistics,
• Computational Geometry,
• Scalable Optimization.

Publications

2026

• Matthijs Ebbens, Jie Lu, Alexander Munteanu.
  Dimension Reduction for Curves: Simplified and Generalized.
  European Symposium on Algorithms (ESA), 2026.
   
• Alexander Munteanu, Matteo Russo, David Saulpic, Chris Schwiegelshohn.
  Terminal Dimension Reduction for Time Series with Applications.
  International Conference on Machine Learning (ICML), 2026.
   
• Alexander Munteanu, Simon Omlor, Jeff M. Phillips.
  Hardness of high-dimensional linear classification.
  International Symposium on Computational Geometry (SoCG), 2026.
   
• Zeyu Ding, Katja Ickstadt, Nadja Klein, Alexander Munteanu, Simon Omlor.
  Scalable learning of multivariate distributions via coresets.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2026.
   
• Zeyu Ding, Simon Omlor, Katja Ickstadt, Alexander Munteanu.
  Scalable Bayesian p-generalized probit and logistic regression.
  Advances in Data Analysis and Classification, 20:109-143, 2026.

2025

• Amer Krivošija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn.
  Improved learning via k-DTW: a novel dissimilarity measure for curves.
  International Conference on Machine Learning (ICML), 2025.

2024

• Han Cheng Lie, Alexander Munteanu.
  Data subsampling for Poisson regression with pth-root-link.
  Advances in Neural Information Processing Systems (NeurIPS), 2024.
   
• Sven Teschke, Katja Ickstadt, Alexander Munteanu.
  Detecting interactions in high-dimensional data using cross leverage scores.
  Biometrical Journal, 66(8):e70014, 2024.
   
• Alexander Munteanu, Simon Omlor.
  Optimal bounds for ℓₚ sensitivity sampling via ℓ₂ augmentation.
  International Conference on Machine Learning (ICML), 2024.
   
• Alexander Munteanu, Simon Omlor.
  Turnstile ℓₚ leverage score sampling with applications.
  International Conference on Machine Learning (ICML), 2024.
   
• Susanne Frick, Amer Krivošija, Alexander Munteanu.
  Scalable learning of Item Response Theory models.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

2023

• 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.

2022

• 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.

2021

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

2020

• 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.

2019

• 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 Krivošija, Alexander Munteanu.
  Probabilistic smallest enclosing ball in high dimensions via subgradient sampling.
  International Symposium on Computational Geometry (SoCG), 2019.

2018

• 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.

2017

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

2016

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

2014

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

Teaching

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 via email.

During my appointment as W3 Substitute Professor of Efficient Algorithms, I taught the following classes at the University of Cologne:

• Summer 2025 (University of Cologne): Lecture "Algorithms and Data Structures", Lecture "Randomized Algorithms" & Seminar for doctoral and final thesis students
• Winter 2024/25 (University of Cologne): Lecture "Efficient Algorithms", Seminar "Theory of Machine Learning" & Seminar for doctoral and final thesis students

My ongoing teaching activity at TU Dortmund University is listed below:

• Winter 2026/27: Lecture "Machine Learning Theory" & Colloquium of the Center for Data Science & Simulation
• Summer 2026: Lecture "Introduction to Statistical Learning" & Colloquium of the Center for Data Science & Simulation
• Winter 2025/26: Colloquium of the Center for Data Science & Simulation
• Summer 2024: Lecture "Introduction to Statistical Learning" & Colloquium of the Center for Data Science & Simulation
• Winter 2023/24: Colloquium of the Center for Data Science & Simulation
• Summer 2023: Colloquium of the Center for Data Science & Simulation
• Winter 2022/23: Lecture "Statistical Learning for Big Data (aka Big Data Analytics)" & Colloquium of the Dortmund Data Science Center
• Summer 2022: Lecture "Introduction to Statistical Learning" & Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Winter 2021/22: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 2021: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Winter 2020/21: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 2020: Colloquium of the Dortmund Data Science Center
• Winter 2019/20: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 2019: Colloquium of the Dortmund Data Science Center
• Winter 2018/19: Substitute for the seminar "Algorithmen zur präferenzbasierten Entscheidungsfindung"