The field of uncertainty quantification and PDE solvers is moving towards more efficient and accurate methods for approximating solutions to complex problems. Researchers are exploring new techniques for density estimation, stochastic PDE solvers, and adaptive unstructured tessellations to improve the accuracy and speed of simulations. Notably, innovative methods such as statistically weighted off-centered estimators and iterative algorithms for constructing unstructured tessellations are being developed to tackle challenging problems in geometry processing, graphics, and nuclear femtography. These advancements have the potential to significantly decrease computation time and improve the accuracy of simulations. Some noteworthy papers in this area include: The paper on finite element analysis of density estimation using preintegration for elliptic PDE with random input, which provides a comprehensive analysis of the finite element error and its combination with quasi-Monte Carlo error. The paper on off-centered WoS-type solvers with statistical weighting, which proposes a principled weighting strategy to balance bias and variance in stochastic PDE solvers.