Welcome to the 7th Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik
24 to 28 March 2025 in Berlin, Germany
The 7th Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik will take place at Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, from 24 – 28 March 2025. It is hosted by Humboldt-Universität zu Berlin in close collaboration with Charité – Universitätsmedizin Berlin and Statistisches Bundesamt. We are looking forward to your participation at the DAGStat conference in 2025.
This year's DAGStat2025 conference will feature a variety of scientific sections, reflecting key areas of current research in statistics. Below is the list of sections that will be covered during the conference.
Advanced Regression Modelling
Artificial Intelligence and Machine Learning
Bayesian Statistics
Bioinformatics and Systems Biology
Causal Inference
Clustering and Classification
Deep Learning + Statistics
Design of Experiments and Clinical Trials
Empirical Economics and Applied Econometrics
Extreme values and rare events
Marketing and E-Commerce
Mathematical Statistics
Meta-Analysis
Network Analysis
Nonclinical statistics
Official and Survey Statistics
Regional and urban statistics
Robust and Nonparametric Statistics
Spatial and spatio-temporal Statistics
Statistical Literacy and Statistical Education
Statistical Methods in Epidemiology
Statistical Software
Statistics in Agriculture and Ecology, Environmental Statistics
Statistics in Finance
Statistics in Social, Behavioral and Educational Sciences
Statistics in the Pharmaceutical and Medical Device Industry
Structural Equation Modelling and latent variables
Survey Methodology
Survival and Event History Analysis
Synthetic data, Georeferencing & Disclosure control
David Blei is a Professor of Statistics and Computer Science at Columbia University and a member of the Columbia Data Science Institute. He earned his Ph.D. in Computer Science from the University of California, Berkeley, in 2004. Following this, he spent two years as a Postdoctoral Fellow in the Department of Machine Learning at Carnegie Mellon University. From 2006 to 2014, he served as an Assistant and later Associate Professor in the Department of Computer Science at Princeton University. In 2014, he joined Columbia University as a Full Professor. Blei is an expert in Machine Learning and Bayesian Statistics, with research focused on Topic Models, Probabilistic Modeling, and Approximate Bayesian Inference.
Xiao-Li Meng
Xiao-Li Meng is a Professor of Statistics at Harvard University and the founding editor of the Harvard Data Science Review, a newly established journal in the field of data science. In 2001, he received the COPSS Award (Committee of Presidents of Statistical Societies) and has since garnered numerous accolades for his over 150 publications across various theoretical and methodological areas. Meng is also recognized for his contributions to education and training, and in 2020, he was elected to the American Academy of Arts and Sciences. His research interests span from the theoretical foundations of statistical inference (e.g., Bayesian, fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase, and multi-resolution inference) to statistical methods and computation (e.g., posterior predictive p-values, the EM algorithm, and Markov Chain Monte Carlo methods), with applications in natural and social sciences, medicine, and engineering.
Susan Murphy
Susan Murphy is the Mallinckrodt Professor of Statistics and Computer Science at Harvard University and an Associate Faculty member at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard. She is a renowned expert in causal inference and the development of statistical methods for clinical trials targeting chronic and recurring diseases. Her work focuses on data-driven algorithms and methods for sequential decision-making in healthcare, such as real-time algorithms for optimizing personalized treatment sequences delivered via mobile devices.
Silvia Richardson
Sylvia Richardson is the Emeritus Director of the MRC Biostatistics Unit (BSU) at the University of Cambridge, where she served as Director from 2012 to 2021. During this time, she also held a professorship at Cambridge. Before joining the BSU, she was the Chair of Biostatistics at Imperial College London from 2000 and previously served as Directeur de Recherches at the French National Institute for Medical Research (INSERM), where she held research positions for 20 years. In 2019, Richardson was appointed Commander of the Most Excellent Order of the British Empire (CBE). She has also received the Royal Statistical Society’s Guy Medal in Silver and a Royal Society Wolfson Research Merit Award. Richardson is a Fellow of the Institute of Mathematical Statistics and the International Society for Bayesian Analysis.
Invited Speakers
Section
Name
Institution
Advanced Regression Modelling
Genevera Allen
Rice U, Houston, USA
Artificial Intelligence and Machine Learning
Luc de Raedt
KU Leuven, BE
Bayesian Statistics
Antonio Pievatolo
IMATI, IT
Causal Inference
Helene Rytgaard
U Copenhagen, DK
Clustering and Classification
Marta Nai Ruscone
U Genoa, IT
Deep Learning + Statistics
Mihaela van der Schaar
U Cambridge, UK
Design of Experiments and Clinical Trials
Anastasia Ivanova
U N Carolina, Chapel Hill, USA
Empirical Economics and Applied Econometrics
Jörg Stoye
Cornell U, Ithaca, USA
Extreme values and rare events
Philippe Naveau
LSCE, Gif-sur-Yvette, F
Marketing and E-Commerce
Daniel Guhl
HU Berlin, D
Mathematical Statistics
Ingrid van Keilegom
KU Leuven, BE
Meta-Analysis
James Pustejovsky
U Wisconsin-Madison, USA
Network Analysis
Philip Leifeld
U Manchester, UK
Nonclinical statistics
Leonard Held
U Zurich, CH
Official and Survey Statistics
Monica Pratesi
ISTAT, Rome, IT
Regional and urban statistics
Eva Heinen
TU Dortmund, D
Robust and Nonparametric Statistics
Marc Buyse
IDDI, Brussels, BE
Spatial and spatio-temporal Statistics
Abhirup Datta
Johns Hopkins U, Baltimore, USA
Statistical Literacy and Statistical Education
Travis Weiland
U N Carolina, Charlotte, USA
Statistical Methods in Epidemiology
Anne Helby Petersen
U Copenhagen, DK
Statistics in Agriculture and Ecology, Environmental Statistics
María Xosé Rodríguez Álvarez
U Virgo, ES
Statistics in Finance
Michael Weber
U Chicago, USA
Statistics in Social, Behavorial and Educational Sciences
Jennie Brand
UCLA, USA
Statistics in the Pharmaceutical and Medical Device Industry
Philip Young
Biontech, Munich, D
Structural Equation Modelling and latent variables
Andreas M. Brandmaier
Medical School, Berlin, D
Survey Methodology
Peter Lugtig
Utrecht, NL
Survival and Event History Analysis
Morten Storm Overgaard
Aarhus U, DK
Synthetic data, Georeferencing & Disclosure control
Jörg Drechsler
IAB, Nuremberg, D
Testing and scaling
Wim van der Linden
U Twente, NL
Text mining, NLP and content analysis
Benjamin Roth
U Vienna, AU
Time Series and Statistical Forecasting
Johanna Ziegel
ETH, Zurich, CH
Trustworthy Data Science
Virginia Dignum
Umeå U, S
Visualisation and Exploratory Data Analysis
Dianne Cook
Monash U, AUS
Tutorials
Monday is Tutorial Day. These are the Tutorials available:
Morning Tutorials
Target trial emulation and causal inference for time-dependent treatments
Social Media and Statistics - How Do They Fit Together?
Generalized pairwise comparisons: A practical guide to the design and analysis of patient-centric trials
Reproducible Research in R: How to Do the Same Thing More Than Once
Afternoon Tutorials
Enhancing your Code: Combining R and C++ via Rcpp and RcppArmadillo
Nonparametrics: Some basics and new developments - common misunderstandings, pitfalls, and surprising results
Variable selection and prediction modelling for high-dimensional genomic data
Full Day Tutorials
Distributional Regression – Models and Applications
An introduction to estimands and estimand-aligned estimation
From Theory to Practice: Vine Copula Models
Introduction to Machine Learning with R and mlr3
Bayesian Data Analysis
Program Overview
under construction
Emil Julius Gumbel
As part of this year’s conference, we are pleased to feature a special exhibition on Emil Julius Gumbel (1891-1966).
Gumbel was a German statistician known for his political activism and pacifism. His work on extreme values and the Gumbel distribution named in his honor continue to be influential in many fields today. Combining his statistical knowledge and political advocacy, he published the book “Vier Jahre politischer Mord (Four years of political murder)” in 1922, showing the judiciary system of the Weimar Republic to be heavily biased in favor of right-wing extremists.
His continued outspoken engagement against fascism led to him being forced out of his tenure at the University of Heidelberg in 1932 after the “Gumbel riots” instigated by the National Socialist German Student League. After Jewish-born Gumbel moved to Paris with his family, his works were subject to Nazi book burnings. In 1940 he escaped the German invasion by fleeing to the US, where he received a professorship at Columbia University in 1953.
Gumbel’s legacy was honored with a Stolperstein, a plaque commemorating the victims of Nazi persecution, in front of his home in Heidelberg in 2021.
In times of increased political division and resurgence of anti-democratic sentiment, it is ever more important to keep the legacy of anti-fascist activists alive. At Humboldt-Universität zu Berlin and Charité - Universitätsmedizin Berlin, we are committed to the values of democracy and respectful coexistence. We are against exclusion and hatred.
The exhibition will be open from 24 March - 14 May 2025. We’re looking forward to sharing this very special experience with you and encourage all conference attendees to join us at:
Lichthof (Ost) / Ausstellungsraum
Humboldt-Universität zu Berlin
Hauptgebäude, Erdgeschoss
Unter den Linden 6, 10117 Berlin
Young Statisticians
At the DAGStat 2025 in Berlin, the Early Career Working Group (AG Nachwuchs) of the IBS-DR will once again organize a Young Statisticians Session (YSS).
In this session, early career statisticians will present their projects in a friendly atmosphere and have the opportunity to receive feedback on their presentations (including content, slide design, etc.). The awarded Young Statisticians will receive a certificate and be invited to the conference dinner. Additionally, their conference fee will be waived. The application deadline is November 10th, 2024 . Further information can be found in the attached flyer.
For questions regarding the application process, the AG Nachwuchs is happy to help at ag-nachwuchs@googlegroups.com.
Flyer
Scientific Committee
The scientific committee consists of representatives of the member societies of the German Consortium in Statistics:
Sessions will take place in the following buildings of the Humboldt-Universität zu Berlin:
Main Building UDL6: Unter den Linden 6, 10117 Berlin
University Building at Hegelplatz: Dorotheenstraße 24, 10117 Berlin
Department of Library and Information Science: Dorotheenstraße 26, 10117 Berlin
Conference Fees
Conference fees will be:
Member
Non-member
Students
Retired
Early bird (until 10 January 25)
300 €
455 €
100 €
100 €
Regular registration (from 11 January until 24 February 25)
390 €
520 €
140 €
140 €
Late/On-site registration
450 €
600 €
150 €
150 €
Note: Speakers are asked to register by 31 January 2025
Important Dates
Registration
From
To
Early bird
to be announced
10 January 2025
Regular registration
11 January 2025
24 February 2025
Late/On-site registration
25 Febraury
Note: Speakers are asked to register by 31 January 2025
Abstract submission
Details on the main conference sections can be found on the conference program page
Conference Flyer
Reception
On Tuesday, 25th of March 2025, a Welcome Reception will take place at Thaersaal, Invalidenstraße 42, 10115 Berlin
Conference Dinner
under construction
Guided Tours
under construction
Things to do in Berlin
Berlin, a city rich in history and culture, offers a vibrant tapestry of art and architecture. From its iconic landmarks to hidden gems, Berlin’s streets tell stories of its tumultuous past and dynamic present. Explore the capital’s diverse neighborhoods, each with its own unique flavor, through a variety of guided tours that cater to all interests.
Information pursuant to Sect. 5 German Telemedia Act (TMG)
Humboldt – Universität zu Berlin
School of Business and Economics
Chair of Statistics
Unter den Linden 6
10099 Berlin
https://desbi.de/ Represented by:
Prof. Dr. Sonja Greven Contact
Phone: + 49 (0) 30 2093 – 99489
E-Mail: eliza.mandieva@hu-berlin.de VAT ID
Sales tax identification number according to Sect. 27 a of the Sales Tax Law:
DE 137176824 Web content manager
Eliza Mandieva
Chair of Statistics
School of Business and Economics
Humboldt-Universität zu Berlin
Spandauer Straße 1
D-10178 Berlin EU dispute resolution
The European Commission provides a platform for online dispute resolution (ODR): https://ec.europa.eu/consumers/odr/. Picture credits: Home page
Humboldt-Universität zu Berlin, University of Potsdam, TU Berlin, TU Dortmund,
Hasso Plattner Institute, Charité, Fraunhofer HHI, Max Dellbrück Center, Wikimedia
Picture credits: Background Images
Photos by Stefan Klenke. License URL: www.hu-berlin.de.
Photos and terms of use can be found here.