Christoph Staudt
I bridge tensor-network and inference research with production-ready software, web applications, and mission-driven product work.
I am a PhD student in machine learning with a focus on tensor networks, distributed computing, and scaling inference for probabilistic models. In parallel, I co-founded Kiron Open Higher Education and spent more than a decade building modern web products across the stack for startups, research groups, and mission-driven organizations, with a strong personal drive toward social impact and public-interest technology.
PhD Student, ML Systems Researcher, Founder, and Full-Stack Engineer
Awards that span research and public-interest entrepreneurship
ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning
ASPLOS / EuroSys
Awarded for systems-oriented work on high-performance intra-operator parallelism in distributed deep learning, aligning with my research on scalable execution and ML infrastructure.
Scholarship
Studienstiftung des deutschen Volkes
Supported by the German Academic Scholarship Foundation in recognition of academic excellence and interdisciplinary promise.
German Gruenderpreis
Kiron Open Higher Education
Recognition for building Kiron into a high-impact education initiative widening access to university-level learning for refugees and underserved communities.
A practice shaped by research depth, software craft, and social impact
My work sits between theoretical computer science, machine learning systems, and the realities of building software people can rely on. I enjoy going deep on hard research questions, but I care just as much about turning ideas into robust tools, usable web applications, and products that hold up in practice.
That combination also shapes how I lead and collaborate. Beyond research and engineering, I am motivated to create systems that are rigorous, useful, and capable of generating real social impact.
Selected papers with an emphasis on first-author work
Exploiting Dynamic Sparsity in Einsum
Christoph Staudt, Mark Blacher, Tim Hoffmann, Kaspar Kasche, Olaf Beyersdorff, Joachim Giesen
Introduces a hybrid einsum execution strategy that switches tensor representations based on evolving sparsity, outperforming static dense or sparse approaches on benchmark workloads.
Improved Cut Strategy for Tensor Network Contraction Orders
Christoph Staudt, Mark Blacher, Julien Klaus, Farin Lippmann, Joachim Giesen
Presents a new graph-cut strategy for tensor-network contraction planning that reduces floating-point cost and avoids expensive runtime hyperparameter tuning.
Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines
Mark Blacher, Christoph Staudt, Julien Klaus, Maurice Wenig, Niklas Merk, Alexander Breuer, Max Engel, Sören Laue, Joachim Giesen
Introduces an open benchmark suite, generators, and converters for evaluating tensor execution engines on a broader and more realistic range of einsum workloads.
Selected work across nonprofit leadership, research tooling, and client delivery
Kiron Open Higher Education
Technical co-founder of the edtech nonprofit building digital pathways into higher education and the job market for refugees and underserved communities.
Led platform architecture and product development for a mission-driven organization operating at meaningful social scale.
Einsum.org and TI2 Group Web Tooling
Designed and built the public web tools, benchmarks, and research-facing interfaces for the TI2 research group.
Turned research software into accessible public-facing tooling that supports exploration, communication, and reproducibility.
Freelance Full-Stack Engineering
Builds web applications, APIs, and data-heavy systems for product teams that need hands-on senior implementation across frontend and backend.
Combines startup execution speed with research discipline, covering architecture, implementation, and delivery.