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BIFOLD Update| July 25, 2025

Researcher Spotlight: Dr. Arnab Phani

Dr. Arnab Phani is a Postdoctoral Researcher at BIFOLD, where he addresses data management challenges in modern AI. Building directly on his foundational PhD work at the DAMS Lab, his current research at the DEEM Lab focuses on enhancing runtime efficiency and fostering responsible data management practices across the entire machine learning pipeline - from data cleaning and validation to training and inference.

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Machine Learning| July 22, 2025

XAI 2025: Best Paper Award

Congratulations to BIFOLD researchers Simon Letzgus, Klaus-Robert Müller and Grégoire Montavon, whose publication: XpertAI: uncovering regression model strategies for sub-manifolds won the BestPaperAward at the  3rd World Conference on eXplainable Artificial Intelligence. BIFOLD researchers contributed on several levels to this leading conference on XAI. 

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Machine Learning| July 17, 2025

Even the smallest number can make a big difference

Minor deviations in backend libraries like CUDA or MKL can cause identical AI models to produce different outputs. At ICML 2025, BIFOLD researcher Konrad Rieck showed how such subtle imprecisions can be exploited—posing a significant risk to AI system security.

©Wojciech Samek
Machine Learning| July 16, 2025

Horses, airplanes, and the question of what explainable AI actually explains

The desire to peek inside the "black box" of AI, to understand what a model has learned and how it makes decisions, is nearly as old as AI itself. BIFOLD and the Fraunhofer Heinrich Hertz Institute (HHI) not only developed one of the first methods (Layer-wise Relevance Propagation (LRP)), to systematically explain the decisions of neural networks, they also played an important role in the past and future of XAI.

s.kurfess
Data Management| July 15, 2025

From Research to Practice

Barrie Kersbergen's PhD thesis on scalable session-based recommender systems powers real-time recommendations at one of Europe's leading retailers — impactful research with direct industry applications and open-source results, co-supervised by BIFOLD´s research group lead, Sebastian Schelter.

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BIFOLD Update| July 11, 2025

ICML 2025 Conference Contributions

BIFOLD researchers from the DAMS, DEEM, ML and MLsec groups will present several contributions at the 42nd International Conference on Machine Learning (ICML 2025). The conference will take place July 13–19, 2025, in Vancouver, Canada.

BIFOLD Update| July 10, 2025

HPI appoints BIFOLD fellow Eleni Tzirita Zacharatou as professor

Prof. Dr. Eleni Tzirita Zacharatou, BIFOLD Junior Fellow and former postdoctoral researcher in the DIMA group, has been appointed W2 Professor at the Hasso Plattner Institute, University of Potsdam. Her research focuses on advancing spatial data science through systems and abstractions that support efficient, large-scale data analysis, with applications in sustainability, equity, and social dynamics.

BIFOLD Update| July 03, 2025

NebulaStream Goes Open Source

NebulaStream, an extensible, high-performance streaming engine for multi-modal edge applications is now open-source. Developed for the Internet of Things by BIFOLD, DIMA, and DFKI researchers, this novel stream processing engine eases the analysis of sensor data in real-time. The source code is now freely available under an Apache 2.0 license.

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BIFOLD Update| July 02, 2025

Looking Back at SIGMOD/PODS 2025 in Berlin

Between June 22nd and June 27th, Berlin hosted members of the international data management community at this year’s SIGMOD/PODS conference, one of the leading conferences in data management. With inspiring keynotes, vibrant exchange, and a record turnout, it was a fantastic week of science and collaboration. We are proud to have co-organized this landmark event!

Explainable AI| June 25, 2025

New Whitepaper on XAI

Prof. Dr. Wojciech Samek is co-author of the new white paper “Explainable AI”, published today by the Plattform Lernende Systeme. Together with colleagues from the “Technological Enablers and Data Science” working group of the Platform Lernende Systeme, they explored different application areas, guided by the question: Who should be told what, how and for what purpose?