Home  | News

07.07.2025

Tiny logo
Teaser image to Anne-Laure Boulesteix Receives Reinhart Koselleck Grant

Anne-Laure Boulesteix Receives Reinhart Koselleck Grant

Funding for Innovative Statistical Research

MCML Principal Investigator Anne-Laure Boulesteix has been awarded a prestigious Reinhart Koselleck Grant by the German Research Foundation (DFG). The highly competitive program supports researchers with an outstanding scientific track record who pursue particularly innovative or high-risk projects.

Her project, titled “The design, interpretation and reporting of empirical studies evaluating statistical methods”, focuses on how studies that test statistical methods are planned, analyzed, and reported. The project is expected to connect well with MCML’s work on Open Science, EmpiricalML, and metascience.

Congrats from us!

#award #research #boulesteix
Subscribe to RSS News feed

Related

Link to

19.01.2026

MCML at AAAI 2026

MCML researchers are represented with 9 papers at AAAI 2026 (6 Main, and 3 Workshops).

Link to MCML PI Frauke Kreuter Featured on ARD alpha on AI

19.01.2026

MCML PI Frauke Kreuter Featured on ARD Alpha on AI

MCML PI Frauke Kreuter featured on ARD alpha discussing AI in daily life, workplace applications, and responsible, future-ready use.

Link to Blind Matching – Aligning Images and Text Without Training or Labels

15.01.2026

Blind Matching – Aligning Images and Text Without Training or Labels

CVPR 2025 research from Daniel Cremers’ group shows how images and text can be aligned without training data, labels, or paired examples.

Link to MCML PIs Featured in Süddeutsche Zeitung

12.01.2026

MCML PIs Featured in Süddeutsche Zeitung

MCML PIs Xiaoxiang Zhu and Felix Dietrich featured in Süddeutsche Zeitung for TU Munich’s 2025 breakthroughs in AI and data science.

Link to High-Res Images, Less Wait: A Simple Flow for Image Generation

08.01.2026

High-Res Images, Less Wait: A Simple Flow for Image Generation

ECCV 2024 research led by Björn Ommer’s team enables faster high-resolution image generation by boosting diffusion models with flow matching.

Back to Top