Tim Weiland
Tim Weiland is a PhD student in the Methods of Machine Learning group at the University of Tübingen, where he works on scalable probabilistic PDE solvers.
His research combines Bayesian inference, sparse linear algebra, and physics-informed priors to make uncertainty quantification practical for large-scale scientific computing problems.
Session
Bayesian spatial modeling is critical across science, yet most practitioners are locked into R due to R-INLA.
We present a Julia ecosystem to change this: GaussianMarkovRandomFields.jl provides fast sparse precision-based inference via SPDE discretizations & more, while IntegratedNestedLaplace.jl brings the full INLA methodology to Julia with a familiar formula interface.
We demonstrate the ecosystem on spatial disease mapping, showing competitive results with R-INLA and native Julia advantages.