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Jun.-Prof. Dr. Alexander Kreiß

Junior Professor

Juniorprofessur für Statistik (JP)
Neues Augusteum
Augustusplatz 10, Room A 437
04109 Leipzig

Phone: +49 341 97 - 32328


Since 2022 I'm Junior Professor for Statistics at the Institute of Mathematics of Leipzig University, where I am part of the Stochastics group. There I conduct my reserach on the statistical analysis of network data (cf. Research Profile). Furthermore, I teach classes about mathematical statistics, statistcal network analysis and causal inference for students of the Diploma programs in mathematics and ecnomical mathematics. I also supervise their Diploma thesis.

Before coming to Leipzig I've had PostDoc positions at The London School of Economics and Political Science (UK), KU Leuven (Belgium), and Mannheim University. I've started my academic career at Heidelberg University, where I did my PhD in 2019 under the supervision of Enno Mammen.

Professional career

  • 10/2018 - 01/2020
    Academic Employee at University of Mannheim
  • 02/2020 - 06/2021
    Academic Employee at KU Leuven
  • 07/2021 - 03/2022
    Research Officer at The London School of Economics and Political Science (LSE)
  • since 04/2022
    Juniorprofessor for Statistics at Leipzig University


  • 10/2015 - 04/2019
    PhD at Heidelberg University

In general my research interests are mostly centred around the analysis of Statistical Models for Network Data, particularly, Semi- and Non-parametric Methods, Dependence, Relational Event Models using counting processes, and Bootstrap methods. In addition, I'm interested in Causal Inference, High-Dimensional Statistics, Quantile Regression, Measurement Error Problems, Hawkes Processes and Survival Analysis. It is particularly interesting if some of these areas overlap.

  • Rothe, C.; Kreiß, A. G.
    Inference in Regression Discontinuity Designs with High-Dimensional Covariates
    The Econometrics Journal. 2022.
    show details
  • Mammen, E.; Polonik, W.; Kreiß, A. G.
    Nonparametric inference for continuous-time event counting and link-based dynamic network models
    Electronic Journal of Statistics. 2019. 13 (2). pp. 2764–2829.
    show details
  • Kreiß, A. G.
    Correlation bounds, mixing and m-dependence under random time-varying network distances with an application to Cox-Processes
    Bernoulli. 2021. 27 (3). pp. 1666–1694.
    show details
  • Kreiß, A. G.; Van Keilegom, I.
    Semi-Parametric Estimation of Incubation and Generation Times by Means of Laguerre Polynomials
    Journal of Nonparametric Statistics. 2022. 34 (3). pp. 570–606.
    show details
  • Mammen, E.; Polonik, W.; Kreiß, A. G.
    Testing For Global Covariate Effects in Dynamic Interaction Event Networks
    Journal of Business & Economic Statistics. 2023.
    show details

more publications

  • Statistical Network Analysis

    Aim of this course is to discuss problems relating to statistical network analysis. Networked data can for example appear in the anaylsis of social networks or economic applications. Possible topics are: Descriptive statistics of networks, network-sampling, models for network data, clustering. The concenpts of the lectures will be illustrated through examples.

    The first half of the course is a lecture-class, the second half of the course is a seminar.

  • Mathematical Statistics

    In the first part of the course we want to understand the general, asymptotic theory of estimators in parametric models: We consider maximum likelihood estimation and show that it is optimal in a certain sense.

    In the second part we study linear models and their extensions (generalised linear models and so-called random effects). Those play an important role in many applications.

    In the last part of the course we discuss non-parametric kernel estimators.

  • Causal Inference in Statistics

    It is often of interest to understand if one variable is causal for another, e.g., is revising for an exam causal for the grade. The main objective of this course is to understand when estimators allow for this interpretation. To answer these and related questions we will discuss do-calculus and often used concepts like the potential outcomes framework, instrumental variables and differences-in-differences.

Research fields



Ich beschäftige mich hauptsächlich mit der statistischen Analyse von Netzwerkdaten. Außerdem interessiere ich mich für nicht-parametrische Probleme.

Contact for media inquiries

Phone: 0341-9732-328