Category: Dissertation Defense

FONDA PhD Defense: Fabian Lehmann on “Adaptive Scheduling of Dynamic Workflows”

Fabian Lehmann defended his dissertation, “Adaptive Scheduling of Dynamic Workflows” with distinction on June 29th, 2026. Fabian was a member of subproject B5 in FONDA Phase I. His work examines the tradeoff between workflow portability and efficiency, and introduces WONDERS, a best of both worlds optimization strategy for Nextflow workflows.

In order for scientific workflows to be portable, workflow management systems such as Nextflow cannot rely on hard-coded assumptions about the dataset or underlying infrastructure to improve efficiency. WONDERS utilizes the Common Workflow Scheduling Interface (CWSI), developed by Fabian, to bring workflow context to the resource manager, then combines his three novel approaches for workflow optimization: WOW, PONDER, and SCALE.

WOW is a scheduling approach which reduces network congestion within a cluster by basing scheduling decisions primarily on the co-location of tasks and data. PONDER and SCALE are tools which predict and minimize the ongoing use of memory (PONDER) and CPU (SCALE) over the course of a workflow execution. The combination of the three techniques in WONDERS results in super-additive improvements in workflow makespan (average reduction ~50% compared to nf-core default settings) when tested on a variety of Nextflow workflows using real-world data from remote sensing and bioinformatics.

Congratulations Fabian!

FONDA PhD Defense Ahn Duc Vu: Towards validating and debugging scientific data analysis workflows

On May 19th, 2026 former FONDA Phase I student Anh Duc Vu successfully defended his dissertation “Towards validating and debugging scientific data analysis workflows” at the university of Bern. The dissertation is available in the University of Bern repository (BORIS Portal).

Congratulations Duc!

FONDA PhD Defense: Sebastian Müller on “Metamorphic Testing for Scientific Software Using Geometrically Representable Input Data”

On June 18th, 2026 Sebastian Müller from FONDA Phase I, subproject A3, successfully defended his dissertation “Metamorphic Testing for Scientific Software Using Geometrically Representable Input Data”. His work focused on testing research software, which can become “untestable” with oracle-based testing methods as a result of its complex and exploratory nature.

Sebastian demonstrated that research software that uses geometrically representable input data (such as images, coordinate grids, or volumetric models) can be tested by applying invariant geometric transformations to the input data. If the software is doing what it is supposed to, the output from the transformed data should then be extremely similar to the original output. He also developed GeoMetaMorph, a tool for detecting likely invariant transformations to use in such tests.

Congratulations Sebastian!

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!

FONDA PhD student Martin Kuban successfully defends his dissertation on “Classification of materials based on similarity measures”

Martin Kuban defended his doctoral thesis on April 15, 2025. He is a member of the Theoretical Solid-State Physics group at Humboldt-Universität zu Berlin. His work focused on extracting comparable “fingerprints” for materials from heterogeneous data sources in order to identify compounds which may have similar properties. As part of this work, he developed MADAS, a python framework providing a modular and extendable interface for similarity calculations in material science.

His contributions to subproject A3 in FONDA include automating this technique as a workflow to calculate similarity between different instances of the same material in an open source repository, where its features have been calculated using different sets of parameters. This allows for the automated detection of parameters which produce reliable results, and identification of those which introduce artifacts.

His excellent work and presentation earned the grade summa cum laude – with highest honors. Congratulations Martin!

FONDA PhD student Mario Sänger successfully defends his PhD thesis on “Representation Learning for Biomedical Text Mining”

Mario Sänger, a member of the group “Human-computer interaction for Scientific Software”, successfully defended his PhD thesis on November 25, 2024. His work focuses on using representation learning to extract meaningful connections between biomedical entities, such as genes, diseases, proteins, and pharmaceuticals from a corpus of PubMed abstracts, as well as biomedical knowledge bases. In addition to demonstrating the feasibility of this corpus-wide approach, he also benchmarked and tested existing pre-trained language models (PLMs) for sentence-level relation prediction. His results show that additional context from biomedical knowledge databases does not enhance the most robust carefully tuned PLMs.

In FONDA, he collaborated with Prof. Dr. Thomas Kosch, exploring the use of ChatGPT as a tool to support users in designing and implementing scientific workflows.

Congratulations Mario, and all the best!

FONDA PHD student Sarah Kleest-Meißner successfully defends her PhD thesis on “Exploring the Complexity of Event Query Discovery”

Sarah Kleest-Meißner, research group “Logic in Computer Science” at HU Berlin’s computer science department, successfully defended her PhD thesis on September 10, 2024. She proposed an expressive, theoretical query model for sequence data based on subsequences and patterns with variables which captures the core of Complex Event Processing (CEP) languages. Based thereon, she presented an algorithm for solving the task of discovering a query that describes best a given finite set of finite sequences of events. The theoretical basis of her query model enabled a comprehensive analysis of the complexity of event query discovery, whereas a prototypical implementation and an experimental evaluation with synthetic and real-world datasets complemented the formal results.

Congratulations!