The arrival of a new batch of freshmen is a sure sign of autumn and the start of a new academic semester. I am thrilled to announce that this semester should also be my last one as a Ph.D. candidate because I officially submitted my dissertation for preliminary examination last week. In this post, I will look back on summer 2018 and recap the research projects that I completed during that time.

Are you using Jupyter notebooks regularly in your machine learning or data science projects? Did you know that you can work on notebooks inside a free cloud-based environment with a GPU accelerator? In this post, I will introduce you to Google Colaboratory and show you in a few simple steps how to integrate this platform into your daily workflow.

This tutorial will teach you how to create a custom Google Maps based map for visualizing geographic statistical data. Such maps can be a useful tool when developing machine learning models. As a specific example case, we will create a map for visualizing the population density and median household income of postal code areas in Finland.

Matrix diagonalization is a fundamental linear algebra operation with a wide range of applications in scientific and other fields of computing. At the same time, it is also one of the most expensive operations with a formal computational complexity of $\mathcal{O}(N^3)$, which can become a significant performance bottleneck as the size of the system grows. In this post, I will introduce the canonical algorithm for diagonalizing matrices in parallel computing to set the scene for today’s main topic: improving diagonalization performance. With the help of benchmark calculations, I will then demonstrate how a clever mathematical library choice can easily reduce the time needed to diagonalize a matrix by at least 50 %.

The 2018 edition of the CP2K UK user meeting was held two Fridays ago at the University of Lincoln, UK. I will be recaping my experiences of the event in this post.