I spent the past year or so accumulating nice resources for data scientists and developers, and I've decided to collect them for everyone to enjoy. If you want to get one in your feed every day during the Christmas period, make sure to follow my Twitter or my LinkedIn profile. In any case, I'll be collecting them in four […]
Category: algorithms
An Advent Calendar for Developers: day 13 to 18
I spent the past year or so accumulating nice resources for data scientists and developers, and I've decided to collect them for everyone to enjoy. If you want to get one in your feed every day during the Christmas period, make sure to follow my Twitter or my LinkedIn profile. In any case, I'll be collecting them in four […]
A small guide to Random Forest - part 2
This is the second part of a simple and brief guide to the Random Forest algorithm and its implementation in R. If you missed Part I, you can find it here. randomForest in R R has a package called randomForest which contains a randomForest function. If you want to explore in depth this implementation, I […]
A small guide to Random Forest - part 1
I've recently started playing with Kaggle and got curious about one of the most famous classification/regression framework, Random Forest. In a problem of classification or regression, several random decision trees (a "forest") are built and at the end the outputs are combined ("bagging"). The intuition is that randomness and a meaningful quantity of trees will avoid […]
Open Data: CT datasets and prototypes
In my research work, I often find it difficult to get datasets for X-ray CT for method validation, neither simulated and real data. Of course, there's the classic Shepp-Logan phantom, but in many cases it would save a lot of work to download datasets to test one's methods. As for my knowledge, there is no broad […]
Mathematicians Go Hollywood
Did you know that in 2008 a mathematician won an Academy Award? Do you know how to model realistic hair for animation movies or a bomb deflagrating for an action movie? Or you simply need some effective active contour segmentation method? All these questions have in common an effective, yet intuitive, mathematical framework: level set […]
Coding coding coding
Despite the confusing title, in the past days I have been coding. A lot. Even though I am dying to leak information about what exactly I am working on, I still need to wait few weeks to reveal. I thought anyway to write something about programming from the perspective of an applied mathematician. Research forces you […]
4D tomography: walkthrough of my project - part 3
Here comes the final part of the walkthrough of my current project on dynamic sparse tomography (see also part 1 and part 2). In the previous post I left the question of the choice of the cut-off function hanging. In a classical level set method, would be the Heaviside step function. The Heaviside function is […]
4D tomography: walkthrough of my project - part 2
After talking about motivation (see the first part and then part 3), I will now go into details with the mathematics foundations of the project. The novel tomography reconstruction algorithm I am contributing developing is based on a level set method approach. Level set methods A level set method is an elaborate, yet geometrically intuitive, framework to deal […]
4D tomography: walkthrough of my project - part 1
Last month the greatest event in Inverse Problems ever took place in Helsinki: the Applied Inverse Problems 2015 conference. In addition, I gave my first technical presentation at the 4D tomography minisymposium (find the slides here). I take the chance to write a series of posts as a walkthrough of my project and its current state. The project When […]