Joeri Hermans


My name is Joeri, I am a Master's student Artificial Intelligence at Maastricht University, and currently working as a Technical Student at CERN. My work at CERN mainly focuses on the development of a distributed profiler, and researching / developing distributed machine learning solutions. My scientific interests are in the fields of machine learning, distributed systems, and astronomy. Although I'm not a physicist, I have been passionate about astronomy from a very young age, and I'm working towards combining these fields in the future.

Latest Work

Distributed Keras

Distributed Deep Learning with Apache Spark and Keras. This will allow you to speed up your learning process by employing distributed machine learning.


Neuro-evolution: car

All website visitors (like you), will teach the car to not collide with the neural network and the edges of the browser. This is done by generating a genome which is then simulated in your browser to evaluate its performance.



Easily tag locations in deep space (directories) and easily warp (like the space drive) to them with a few keystrokes.



Distributed Deep Learning - Part 1 - An introduction

In the following blog posts we study the topic of Distributed Deep Learning, or rather, how to parallelize gradient descent using data parallel methods. We start by laying out the theory, while supplying you with some intuition into the techniques we applied. At the end of this blog post, we conduct some experiments to evaluate how different optimization schemes perform in identical situations. We also introduce dist-keras, which is our distributed deep learning framework built on top of Apache Spark and Keras. For this, we provide several notebooks and examples. This framework is mainly used to test our distributed optimization schemes, however, it also has several practical applications at CERN, not only because of the distributed learning, but also for model serving purposes. For example, we provide several examples which show you how to integrate this framework with Spark Streaming and Apache Kafka. Finally, these series will contain parts of my master-thesis research. As a result, they will mainly show my research progress. However, some might find some of the approaches I present here useful to apply in their own work.