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BIFOLD Institute

Fotoquelle: yanalya/Freepik

About BIFOLD

Cross-linking Machine Learning and Big Data Management

The Berlin Institute for the Foundations of Learning and Data (BIFOLD) has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: The Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). It is one of Germany's six national AI centers, receiving permanent funding from the State of Berlin and the Federal Ministry of Education and Research. BIFOLD is based at TU Berlin, institutional partner is the Charité - Universitätsmedizin Berlin. 
Currently BIFOLD consists of 12 researchgroups with >150 employees, a Graduate School and the BIFOLD Office.  It also involves Fellows from Berlin's major universities, Charité - Universitätsmedizin Berlin, as well as various other national and international universities or non-university research institutions.
BIFOLD scientists have received numerous scientific awards, including the Leibniz Prize, Berlin Science Award, Vodafone Prize, Leopoldina membership, and international accolades.

Our Mission
BIFOLD conducts groundbreaking foundational research in Big Data Management (DM) and Machine Learning (ML), as well as their intersection, to educate the talents of the future and to create high-impact knowledge.

Scalable & Agile Research

Data management and machine learning are the scientific and technical pillars powering the current wave of innovation in artificial intelligence (AI); it is the efficient processing and intelligent analysis of very large, complex, heterogeneous data that has the potential to revolutionize and substantially improve our lives and societies. BIFOLD conducts scalable yet agile foundational AI research. Furthermore, it addresses the emerging challenges and requirements created by the rapidly growing importance of data management and machine learning in practically all areas, from medicine, industry, natural sciences, humanities, e-commerce, and media, to government and society. Key thematic areas of BIFOLD include scalable data management (Big Data) and machine learning, management of data science processes, new AI architectures and systems, responsible data management and explainable AI.
 

Our Research Groups:

Database Systems and Information Management

Big Data, Data Streaming, Distributed Data

Machine Learning

Interpretable ML Methods, Data Modeling, Anomaly Dection 

Data Integration and Data Preparation

Databases, Data Integration, Data Mining

Management of Data Science and Processes

Big Data Engineering

Data Science Abstractions and Systems, Performance-Accuracy Tradeoffs in Data Science, Data Cleaning Pipelines and Optimization

Big Data Analytics for Earth Observation

Data Management and Machine Learning for Earth Observation

Machine Learning and Security

Intelligent Security Systems and Attack-Resilient Machine Learning

Explaining Deep Neural Networks

Explainable AI, Machine Learning, Data Science

Probabilistic Modeling and Inference

Probabilistic Modeling, Bayesian Inference, Uncertainty Estimation

Intelligent Biomedical Sensing

Biomedical Engineering & Multimodal Data Analysis for Wearable Neurotechnology

Machine Learning for Molecular Simulation in Quantum Chemistry

Many-Body Dynamics, Physics-Informed Models, Numerical Methods

Distributed Data Stream Processing in Heterogeneous Environments

Heterogeneous Data Streams, Distributed Fog and Edge Environments, IoT Data and Application Management