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Guided exploration of chemical structures
The development of new, stable materials has enabled significant advancements across various research fields. A key challenge in this process is global energy optimization. The Agility Project "Guided Exploration of Chemical Space with Deep Neural Networks and Bayesian Optimization" has contributed to the advancement of current research in this area.
Expert Opinions on the Recent Success of DeepSeek
Experts from BIFOLD and TU Berlin on the difference between open source applications such as DeepSeek and other LLMs, and Europe's role in the development of artificial intelligence (AI).
The Age of AI: Panel Discussion with Sam Altman
Review key insights from Sam Altman (OpenAI), Nicole Büttner (Merantix Monumentum), Fatma Deniz (TU Berlin), and Volker Markl (BIFOLD) as they examine the opportunities, challenges, and societal impact of AI. Watch the panel discussion for an analysis of AI's evolving role in research and industry.
AI improves personalized cancer treatment
Personalized medicine aims to tailor treatments to individual patients. Until now, this has been done using a small number of parameters to predict the course of a disease. A team of researchers from different Universities and BIFOLD has developed a new approach to this problem using artificial intelligence (AI).
Researcher Spotlight: Thomas Schnake
Dr. Thomas Schnake, a postdoctoral researcher at BIFOLD and TU Berlin, is pioneering the future of explainable AI. With his 'Summa Cum Laude' PhD on creating human-readable explanations for machine learning models, Thomas is working to make complex AI behaviors more intuitive and accessible, while uncovering new scientific insights.
BIFOLD researcher co-authors paper on next-generation Query Optimization at CIDR
At CIDR 2025 in Amsterdam, BIFOLD researcher Stefan Grafberger presents a paper co-authored during his Microsoft research internship. The study explores "Query Optimizer as a Service," promising simpler, more efficient data system development.
Feynman Prize for Prof. Dr. Klaus-Robert Müller
The Foresight Institute has awarded Prof. Dr. Klaus-Robert Müller, BIFOLD Co-director and Chair of the Machine Learning group at TU Berlin, with the 2024 Foresight Feynman Prize in Nanotechnology in the category of Theory.
Tackling Data Heterogeneity in Federated Learning
A persistent challenge in Federated Learning (FL) lies in handling statistical heterogeneity—namely, if the clients’ distributions are different from each other. Shinichi Nakajima, BIFOLD research Grouplead and his team propose FLOCO (Federated Learning over Connected Modes), to tackle those issues.
BIFOLD Ph.D. Student Receives Software Campus Funding
Muaid Mughrabi is a Ph.D. student specializing in Data Integration and Data Preparation at BIFOLD, Technische Universität Berlin, under the supervision of Prof. Dr. Ziawasch Abedjan. In the coming year, he will embark on a research project in collaboration with Celonis.
Machine learning accelerates catalyst discovery
Machine learning (ML) models have recently become popular in the field of heterogeneous catalyst design.