Steinar Laenen

I am an MSc Artificial Intelligence student at the University of Edinburgh, broadly interested in machine learning theory & applications. I have a strong background in mathematics, and experience in doing individual & collaborative research in a variety of fields.

Some courses that I am taking/have taken include: Machine Learning and Pattern Recognition, Algorithmic Foundations of Data Science, Algorithmic Game Theory, Probabilistic Modelling and Reasoning, Quantum Computing, Reinforcement Learning, Neural Information Processing, Natural Computing.

This summer I wil be writing my thesis on directed stochastic block models, under supervision of Dr. He Sun.

Here in Edinburgh I'm also part of the autonomous driving team at Edinburgh University Forumula Student , where I co-lead the research team in developing end-to-end controllers using supervised learning and deep reinforcement learning.

At the moment I'm also heavily involved in organising a series of panel discussions titled: We Need to Talk About AI, where we invite insightful speakers to discuss some pressing issues around AI development!

I did my bachelors (summa cum laude) in mathematics and computer science at Amsterdam University College , where I wrote my thesis on explaining structure of the human visual cortex using deep neural networks, supervised by Dr. Steven Scholte .

I will be finishing my degree in August 2019, after which I am still looking for opportunities. If you have anything you think I might be interested in - especially research related - send me a message!

Email  /  CV  /  LinkedIn  /  Google Scholar  /  GitHub


I've done/I am doing projects in machine learning, computer vision, graph theory, virtual agents, virtual reality, NLP, and cognitive science. Here is a selection of some projects.


Reinforcement Learning for the football RoboCup 2D Half Field Offense (HFO) task.
Coursework Project for my Reinforcement Learning course at the University of Edinburgh.

For the coursework we implemented and optimized a wide set of single-agent and multi-agent reinforcment learning algorithms, including some of the canonical ones (Q-learning, SARSA, Value Iteration). We also implemented a state-of-the-art deep RL algorithm ( Asynchronous one-step Q-learning) on which I received a perfect score for implementation and performance.


Supervised and Reinforcement Learning for end-to-end Controllers in Autonomous Cars.
Edinburgh University Formula Student AI Research Team

We've currently implemented a deep ConvNet to drive the car autonomously in ROS simulation using supervised learning, and we have implemented an end-to-end controller that learns to drive a car from scratch using Deep Reinforcement Learning. I can't release any code/report.


Thermal Mapping of Icelandic Geothermal Surface Manifestations with a Drone
Grimur Bjornsson, Gunnar Grimsson, Ari Sigurdsson, Valdimar S. Laenen
44th Workshop on Geothermal Resevoir Engineering Stanford University, 2019

Drone equipped with RGB and infrared cameras are used to create orthomosaic maps of areas that contain geothermal lineaments. Research was done at Warm Arctic ehf.

Over the Christmas Break I developed an anomaly detection tool to automatically detect areas of geothermal importance using deep autoencoders and a one-class SVM.

Interpreting Social Commitment in a Simulated Theater
Daniel Veutgen, Marco Massetti, Joy Rossi, Leonardo Veroli, Athena Ásgeirsdóttir, Gudrun Baldursdóttir, Sigrún Gissurardóttir, Gísli Gudmundsson, Tinna Sigurdardóttir, Valdimar Laenen, Hannes Högni Vilhjálmsson
Proceedings of the 18th International Conference on Intelligent Virtual Agents, 2018  
code /

The research consisted of designing and programming automated intelligent social behaviour for virtual agents in Unity3D and C#. A cool thing that we added to the project was the ability to 'become' one of the characters using an Oculus Rift, and being able to interact with them.


Visual Pathways from the Perspective of Emotion and Physical Feature Detection in Multilabel Deep Neural Networks
Steinar Laenen, Steven Scholte
Bachelor's Thesis, 2018

We use deep neural networks trained on two orthogonal targets to investigate whether seperation of tasks in the brain can be explained.

Based on Jon Barron's website