Dr. Alexander Munteanu
Contact
Office: mathematics building, room E16b
Phone: +49 231 755 - 7885
E-Mail: alexander.munteanu@tu-dortmund.de
Address: Fakultät Statistik
Technische Universität Dortmund
44221 Dortmund
Office hours: by appointment
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.
- Sven Teschke, Katja Ickstadt, Alexander Munteanu.
Detecting Interactions in High-Dimensional Data Using Cross Leverage Scores.
Biometrical Journal, 66(8), 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"