Research Scientist · Computational and Data Science
I study computational methods for automatic differentiation, argument mining, and scalable data-intensive systems, combining rigorous evaluation with practical software implementation.
Computational methods for arguments, automatic differentiation, and scalable data systems
I am a PhD researcher at Friedrich-Schiller University Jena working on piecewise linear automatic differentiation. My broader interests include scientific computing, parallel systems, argument mining, NLP, and evaluation methods for data-intensive research problems.
Investigating computational foundations and implementation strategies for automatic differentiation in advanced computing contexts.
Evaluating how argument summarization methods generalize across datasets and how generated key points can be assessed without references.
Teaching Parallel Computing, Big Data, and Technical Computer Science with a focus on reproducible computational practice.
Selected theses, computational studies, and applied scientific software work
Research appointments and applied engineering work
Research on piecewise linear automatic differentiation within advanced computing, alongside graduate teaching in Parallel Computing, Big Data, and Technical Computer Science.
Designed and deployed a microbenchmarking framework and led the development of an AI-powered internal support chatbot.
Developed computer vision pipelines to enhance image classification performance for industrial quality assurance.
Supported senior leadership with financial analysis, scheduling, and operational coordination across the organization.
Academic training and doctoral research
Doctoral research on piecewise linear automatic differentiation for data-intensive and high-performance computing systems, with teaching responsibilities in Parallel Computing, Big Data, and Technical Computer Science.
Graduate focus on machine learning, statistical inference, and cloud-scale data engineering. Master's thesis: "Analyzing Methods of Key Point Generation for Arguments across Different Datasets".
Undergraduate studies centered on automation engineering, systems design, and quantitative business management.