Tag: machine learning

FONDA Winter Lecture Series Continues into 2026!

On January 20th at 15:00, FONDA will continue our Winter Lecture Series. We will meet at HU-Main Campus, Unter den Linden 6, Room 2249a for three exciting talks by:

  • Laura Koesten – University of Vienna: A Human-Centered Perspective on Data-Centric Sensemaking
  • Gabin An – Korea University: Finding Bugs at Scale: LLM-Based Fault Localization in Real-World Software
  • Karsten Peters-von Gehlen – Deutsches Klimarechenzentrum: Bridging Petabyte-Scale Climate Data and Analysis Workflows with FAIR Digital Objects

The complete schedule can be found here: FONDA Winter Lecture Series and you can also follow the lectures online.

We are looking forward to seeing you there!

FONDA PhD Defense: Jonathan Bader on “Task Resource Prediction for Efficient Execution of Scientific Workflows”

Jonathan Bader defended his doctoral dissertation “Task Resource Prediction for Efficient Execution of Scientific Workflows” with distinction on June 4th, 2025. He is a member of the group “Distributed and Operating Systems” at TU Berlin, where he worked on FONDA subproject B1. His work focuses on predicting which tasks in a workflow are most resource intensive in order to dynamically adjust resource allocation and scheduling.

As part of this research, he introduced Lotaru and Sizey, two novel methods for predicting task run-time and memory requirements, respectively. Lotaru allows researchers to create a sensible baseline resource allocation profile for a workflow based on the task requirements and target infrastructure. Sizey continuously predicts the amount of memory each task requires and adjusts the memory allocation during runtime to minimize over-allocation while also preventing failures. Both outperform previous methods and improve the efficiency of workflow execution.

Congratulations Jonathan!

PI-Lecture Series part 2

Our second set of PI-Lectures will be today at 15:00 at Einstein Center Digital Future. The following PIs will be presenting their research areas:

  • Patrick Hostert – Satellite Remote Sensing
  • Matthias Boehm – System Infrastructure for Data-centric ML Pipelines
  • Tillman Rabl – Carbon-efficient Data Systems
  • Odej Kao – LLMOps for Reliability and Availability of Massive AI Infrastructures