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Machine Learning| August 06, 2024

Simulation of quantum systems

Researchers from the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin and Google DeepMind have now developed a novel machine learning algorithm which enables highly accurate simulations of the dynamics of a single or multiple molecule on long time-scales.

©️ BIFOLD
Strong presence at IGARSS: The Big Earth group, led by Prof. Begüm Demir.
Earth Observation| July 29, 2024

BIFOLD at IGARSS 2024

BIFOLD research group Big Data Analytics for Earth Observation (BigEarth), led by Prof. Begüm Demir, presented four papers at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024) in Athens, Greece. IGARSS 2024 scientific program focused on sustainable development in line with the United Nations 2030 Agenda.

Carbon aware computing relies on clean energy.
Data Management| July 24, 2024

Vessim - A Testbed for Sustainable Computing Systems

One approach to reduce the operational carbon emissions of datacenters is carbon-aware computing: aligning power demand with the availability of clean energy. However, common metrics for describing “clean” energy often oversimplify the complex nature of power grids and energy markets. To enable truly carbon-aware systems, visibility and control over the underlying energy system is needed. BIFOLD researchers developed an open-source simulation environment for research at the intersection of computing and energy systems.

©️BIFOLD
Dr. Christoper Anders
BIFOLD Update| July 23, 2024

How to remove biases from neural networks

Dr. Christopher Anders was PhD candidate in the Machine Learning group of Prof. Dr. Klaus-Robert Müller. At the beginning of the year 2024 he successfully defended his thesis and has now moved on to become a Postdoc in Japan. Before leaving Berlin, he told us what research topics drive him and where one can find him when he's not sitting in front of the computer.

©️Stefan Haufe
Prof. Dr. Stefan Haufe
BIFOLD Update| July 03, 2024

Welcome Prof. Dr. Stefan Haufe

A warm welcome to Prof. Dr. Stefan Haufe as a new BIFOLD Fellow. Stefan Haufe is Professor of computer science and, since 2021, the head of the Uncertainty, Inverse Modeling and Machine Learning (UNIML) group at Technische Universität Berlin as well as Head of the working group Machine Learning and Uncertainty at Physikalisch-Technische Bundesanstalt (PTB) and Head of Brain and Data Science group, Berlin Center for Advanced Neuroimaging (BCAN) - Charité.

 

 

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Cyber Security| July 01, 2024

BIFOLD researchers present four papers at ASIACCS 2024

The 19th ACM ASIA Conference on Computer and Communications Security (ASIACCS 2024) will take place in Singapore from July 1 to July 5, 2024. The conference will focus on specific areas of computer science, such as information security and information privacy.

© BIFOLD
Data Management| June 26, 2024

DEBS 2024 - Keynote by Volker Markl

Lyon, France will host the 18th ACM International Conference on Distributed and Event-Based Systems (DEBS) from June 25 to 28, 2024. BIFOLD Director Volker Markl is honored to offer a keynote on data stream processing.

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BIFOLD Update| June 24, 2024

LNDW 2024: BIFOLD KI-scavenger hunt inspires families

During the Long Night of Science, BIFOLD offered a digital scavenger hunt to present complex AI research topics for both young and old.

© BIFOLD
Data Management| June 13, 2024

BIFOLD Researchers receive three SIGMOD Awards

Each year SIGMOD conference awards are bestowed on researchers who have especially contributed to the field of data management. In 2024 BIFOLD researchers were honored to receive three awards.

©️BIFOLD/Felix Noak
Data Management| June 12, 2024

Newly appointed BIFOLD Professor: Sebastian Schelter

As of June 1st, 2024, Sebastian Schelter is a full Professor at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin. He chairs the Management of Data Science Processes group (DEEM Lab), whose research is focused on the intersection of data management and machine learning.