Lamarr Institute

AI research links mosquitoes and physics: New methods estimate class prevalence under shifting data conditions.

Lamarr PI Jürgen Gall receives the MARVIN Cup for RiverMamba, a project advancing resource-efficient AI models for complex time series. The award highlights the growing importance of high-performance computing infrastructure for scalable and real-world AI applications.

Christian Glaser receives the Wallmark Prize 2026 for his research at the intersection of astroparticle physics and artificial intelligence. His work demonstrates how AI, as an integral part of experimental methodology, enables new approaches to detecting ultra-high-energy cosmic particles.

The ARD PlusMinus report on AI in Industry shows that value creation and competitiveness depend on how consistently AI is integrated into industrial processes.

AI Workshop 2026 in Dortmund highlights how AI can be integrated into workplace processes. Researchers and practitioners discuss human-centered, safe, and practical solutions for the future of work.

The Bonn-based NimbRo team successfully defends its RoboCup title. At the German Robotics Conference, Lamarr researchers highlight how AI and robotics are converging to enable real-world applications.

A Lamarr Institute exhibit on the MS Wissenschaft demonstrates how AI and digital twin models analyze causal relationships in medical data, enabling more personalized treatment decisions and advancing research in AI-driven healthcare.

Machine learning is no longer confined to static datasets. Many modern problems — from energy forecasting and fraud detection to sensor analytics and cybersecurity — involve continuous streams of data. These streams arrive instance by instance, evolve over time, and often undergo concept drift, making traditional batch learning unsuitable. This post is for ML practitioners and applied researchers…

At the University of Bonn’s Schnupper Uni, Prof. Lena Funcke inspired 225 female students with insights into AI research, theoretical physics, and pathways into STEM careers.

This is the third article in our Stream Mining blog series. Following “Learning From Data Streams: Foundations of Stream Learning” by Dr. Sebastian Buschjäger and the second contribution on “Green Online Learning with Heterogeneous Ensembles“, this post dives deeper into Streaming Gradient Boosting. We explore how boosting methods can be adapted to evolving data streams and how recent advances fi…

The research project AALearning (Adversarial and Uncertainty-Aware Learning) has officially started with its kick-off collaboration meeting this week. Funded by the German Federal Ministry of Research (BMFTR) within the ErUM-Data programme, AALearning aims to strengthen the robustness, interpretability, and reliability of machine-learning methods used in fundamental physics research. AALearning b…

Building on Dr. Sebastian Buschjäger’s blog post “Learning From Data Streams: Foundations of Stream Learning”, this second article in our Stream Mining series explores how heterogeneous online ensembles like HEROS enable accurate, resource-efficient learning under concept drift. Why One Model Isn’t Always Enough Relying on a single machine learning model for prediction isn’t always the most relia…

The Lamarr Institute, the University of Bonn, and the Nara Institute of Science and Technology (NAIST) have launched a long-term research collaboration in artificial intelligence, life sciences, and data science. The partnership combines a Memorandum of Understanding, a student exchange agreement, and a cross-appointment bridging research and teaching between Germany and Japan.

New research identifies scaling laws that explain how vision-language models develop emergent multilingual image captioning abilities through generalization from translation data, even without explicit multimodal supervision

Prof. Dr. Julia Tjus has been appointed as an international member of the Royal Swedish Academy of Sciences. Her work at the interface of theoretical physics and artificial intelligence strengthens Lamarr’s international research network.

In most machine learning scenarios, we start from the same premise: We have a dataset – say, a table of images and their labels – and we can look at it as often as we want. We can shuffle it, split it, normalize it, train a model, evaluate it, retrain it… all at leisure. This is what we mi

Funded by the German Federal Ministry of Research, Technology and Space (BMFTR), Physics-LLM is an ErUM-Data research project developing AI-based tools using large language models to improve research data management in physics and to operationalise FAIR data principles across the entire data lifecycle.

Lamarr Principal Investigator Petra Wiederkehr has been elected to Germany’s National Academy of Science and Engineering (acatech).

The European research project RePAIR – Reconstructing the Past combines robotics, AI, and computer vision to reconstruct ancient frescoes from Pompeii using an AI-supported method that digitally analyzes the fragments, identifies matching pieces, and reassembles them using a sensitive two-armed robot.

Award-winning research on sustainable large language models recognized by TU Dortmund for advancing energy-efficient and transparent AI systems.