NEWS
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.
EVENTS
BLISS Speaker Series #13: Making AI fit for the Life Sciences by Prof. Katharina Baum
Prof. Katharina Baum, Professor at FU Berlin, will discuss Making AI fit for the Life Sciences: Informing ML and Explaining Uncertainty
BLISS Speaker Series #14: Learning Physical Laws from Data by Prof. Sven Wang
Prof. Sven Wang, Professor at HU Berlin, will discuss Learning Physical Laws from Data
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.
OPPORTUNITIES
BIFOLD welcomes job applications from interested parties year-round.
PUBLICATIONS
Arnab Phani, Matthias Boehm
MEMPHIS: Holistic Lineage-based Reuse and Memory Management for Multi-backend ML Systems
Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
How Green is AutoML for Tabular Data?
Fatemeh Ahmadi, Marc Speckmann, Malte Fabian Kuhlmann, Ziawasch Abedjan
MaTElDa: Multi-Table Error Detection
GRADUATE SCHOOL
Based on a highly competitive application process, the BIFOLD Graduate School offers an innovative fast-track PhD Program for students holding a bachelor’s degree, as well as a PhD Program for students with a master’s degree.
INSTITUTIONAL PARTNERS
ASSOCIATED PARTNERS
