About me

After receiving my PhD in theoretical computer science under supervision of Christian Sohler, I am now a researcher in the statistics department at 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 & Simulation (DoDaS) as 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. 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,
• convex optimization.

Publications

2024

• Han Cheng Lie, Alexander Munteanu.
  Data subsampling for Poisson regression with pth-root-link.
  Advances in Neural Information Processing Systems (NeurIPS), 2024.
   
• Zeyu Ding, Simon Omlor, Katja Ickstadt, Alexander Munteanu.
  Scalable Bayesian p-Generalized Probit and Logistic Regression.
  Advances in Data Analysis and Classification (ADAC), 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.
  Symposium on Computational Geometry (SoCG), 2019.
  European Workshop on Computational Geometry (EuroCG), 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.
  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.

• Summer 24: Lecture "Introduction to Statistical Learning" & Colloquium of the Center for Data Science & Simulation
• Winter 23/24: Colloquium of the Center for Data Science & Simulation
• Summer 23: Colloquium of the Center for Data Science & Simulation
• Winter 22/23: Lecture "Statistical Learning for Big Data (aka Big Data Analytics)" & Colloquium of the Dortmund Data Science Center
• Summer 22: Lecture "Introduction to Statistical Learning" & Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Winter 21/22: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 21: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Winter 20/21: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 20: Colloquium of the Dortmund Data Science Center
• Winter 19/20: Seminar "Foundations of Data Science" & Colloquium of the Dortmund Data Science Center
• Summer 19: Colloquium of the Dortmund Data Science Center
• Winter 18/19: Substitute for the seminar "Algorithmen zur präferenzbasierten Entscheidungsfindung"