Yves Baumann_

PhD in Computer Science @ ETH Zurich. Randomized numerical linear algebra & spectral graph theory, from theory to high-performance implementations.

Role
PhD · Computer Science
Affiliation
ETH Zurich · A&O Lab
Based in
Zurich, Switzerland
Focus
Spectral graph theory · HPC
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approximate Cholesky · tree fill-in

01 About

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

Currently interested in

Randomized numerical linear algebra Computational spectral graph theory Graph neural networks Power grid optimization High-performance & parallel computing GPU programming GPU acceleration for linear systems

02 Selected publications

TitleVenueYear
Parallel Spectral Graph Sparsification via Low Diameter Decompositions
SPAA
2026
Energy-Optimal and Low-Depth Algorithmic Primitives for Spatial Dataflow ArchitecturesPDF
IPDPS
2025
Low-depth spatial tree algorithmsPDF
IPDPS
2024
A Framework for Parallelizing Approximate Gaussian EliminationPDF
SPAA
2024
The spatial computer: A model for energy-efficient parallel computationarXiv
arXiv
2022

Full list on Google Scholar.

03 Work experience

Quant Engineer · swissQuant Group AG
Performance optimization for derivatives pricing (CPU/GPU) and market/credit risk modeling (VaR/CVaR).
Jul 2024 → Present
Consultant (Financial Services Strategy) · EY
Market analysis and strategy work in Swiss financial services (incl. digital assets).
Oct 2022 → Oct 2023

04 Education

PhD, Computer Science · ETH Zurich
Algorithms & Optimization Group. Randomized numerical linear algebra, AI/ML, Laplacian solvers, parallel & high-performance computing.
Sep 2025 → Present
Visiting Research Scientist · Simons Institute for the Theory of Computing
Research visit in Berkeley, CA (1 month) focused on theory / algorithms.
Oct 2025
MSc, Computer Science · ETH Zurich
Major: Theoretical CS · Minor: Machine Learning. Thesis: "Practical Graph Sparsification for GNNs" (Prof. Kyng), grade 6/6.
Sep 2022 → Sep 2024
BSc, Computer Science · ETH Zurich
Theoretical Computer Science.
Sep 2019 → Sep 2022

05 Talks & teaching

Parallel approximate Cholesky for Laplacian Systems via Independent Sets
Poster · RandNLA Workshop, Bernoulli Center for Fundamental Studies (EPFL), Lausanne
Jun 2026
A Framework for Parallelizing Approximate Gaussian Elimination
Invited talk · Huawei, Switzerland
Jul 2024

06 Outside work

Volleyball Chess Poker