Yves Baumann

Me

Hi, I’m Yves. I’m a PhD student at ETH Zurich (Algorithms and Optimization Lab), working on randomized numerical linear algebra and spectral graph theory, both theory and high-performance implementations, with a focus on fast (parallel) Laplacian solvers.

Previously, I was a Quant Engineer at swissQuant AG working on risk models (asset managers and clearing houses) and performance optimization for pricing engines (CPU and GPU). I completed my MSc in Theoretical CS & ML at ETH Zurich, after a BSc in Computer Science.

Outside of work, I play volleyball, chess and poker.

Research

Selected publications. Full list on Google Scholar.

Energy-Optimal and Low-Depth Algorithmic Primitives for Spatial Dataflow Architectures — IPDPS, 2025 PDF
Low-depth spatial tree algorithms — IPDPS, 2024 PDF
A Framework for Parallelizing Approximate Gaussian Elimination — SPAA, 2024 PDF
The spatial computer: A model for energy-efficient parallel computation — arXiv, 2022 arXiv

Work experience

Quant Engineer, swissQuant Group AG (Jul 2024–Present)

Performance optimization for derivatives pricing (CPU/GPU) and market/credit risk modeling (VaR/CVaR).
Visiting Research Scientist, Simons Institute for the Theory of Computing (Oct 2025)

Visiting researcher in Berkeley, CA (1 month) focused on theory/algorithms.
Consultant (Financial Services Strategy), EY (Oct 2022–Oct 2023)

Market analysis and strategy work in Swiss financial services (incl. digital assets).

Education

PhD, Computer Science — Algorithms & Optimization Group - ETH Zurich (Sep 2025–Present)

Randomized Numerical Linear Algebra, AI / Machine Learning, Laplacian Solvers, Parallel and High-Performance Computing
MSc, Computer Science — Major: Theoretical Computer Science, Minor: Machine Learning ETH Zurich (Sep 2022–Sep 2024)

Thesis: “Practical Graph Sparsification for GNNs” (Prof. Kyng), grade 6/6.
BSc, Computer Science — Theoretical Computer Science - ETH Zurich (Sep 2019–Sep 2022)