Area of research
Nuclear and High Energy Physics · Statistical and Nonlinear Physics
Research interest
Research interests include Computer science, Physics, Quantum chromodynamics, Parallel computing, Lattice gauge theory, and Eigenvalues and eigenvectors.
A novel gauge-equivariant neural network architecture for preconditioners in lattice QCD
<tt> <b>mhn</b> </tt> : a Python package for analyzing cancer progression with Mutual Hazard Networks
Reconstructing Disease Histories in Huge Discrete State Spaces
Gauge-equivariant pooling layers for preconditioners in lattice QCD
Modeling metastatic progression from cross-sectional cancer genomics data
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
Modeling metastatic progression from cross-sectional cancer genomics data
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Gauge-equivariant neural networks as preconditioners in lattice QCD
MRHS multigrid solver for Wilson-clover fermions
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Gauge-equivariant multigrid neural networks
Approximation formula for complex spacing ratios in the Ginibre ensemble
Low-rank tensor methods for Markov chains with applications to tumor progression models
Applying and optimizing the Exa.TrkX Pipeline on the OpenDataDetector with ACTS
Execution‐Cache‐Memory modeling and performance tuning of sparse matrix‐vector multiplication and Lattice quantum chromodynamics on A64FX
New universality classes of the non-Hermitian Dirac operator in QCD-like theories
Machine learning for surface prediction in ACTS
Machine learning for surface prediction in ACTS
Performance Modeling of Streaming Kernels and Sparse Matrix-Vector Multiplication on A64FX
Loss-Function Learning for Digital Tissue Deconvolution
DTD: An R Package for Digital Tissue Deconvolution
Lattice QCD on a novel vector architecture
Lattice QCD on a novel vector architecture
Modelling cancer progression using Mutual Hazard Networks
Induced QCD II: numerical results
University of Regensburg Publication Server (University of Regensburg) 2019cited by 4position: last
Dirac spectrum and chiral condensate for QCD at fixed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>θ</mml:mi></mml:math> angle
Lattice QCD on upcoming ARM architectures