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<h1 class="brand-heading">Rein Houthooft</h1>
<p class="intro-text">Researcher in <span class="rein_green">artificial intelligence</span> / <span class="rein_green">machine learning</span>
</p>
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<h2 class="text-center">About</h2>
<p>
After graduating summa cum laude from <a href="http://www.ugent.be/en" target="_blank">Universiteit Gent, Belgium</a> in 2014 with an engineering degree (burgerlijk ingenieur) in computer science, I obtained a doctorate in the beginning of 2017 at <a href="http://www.ugent.be/ea/idlab/en" target="_blank">IDLab</a>, which was supported by a Doctoral Fellowship of the <a href="http://www.fwo.be/en/" target="_blank">Research Foundation — Flanders (FWO)</a> for fundamental research.
<br><br>
Initially I performed applied research in combinatorial optimization and operations research at the <a href="http://set.kuleuven.be/codes" target="_blank">CODeS</a> group, part of <a target="_blank" href="http://www.kuleuven.be/english/">KU Leuven, Belgium</a>, in collaboration with <a href="http://corporate.arcelormittal.com" target="_blank">ArcelorMittal</a>. Hereafter I worked in the domain of network science, designing experimental routing strategies for computer networks. Later my interests shifted to artificial intelligence and machine learning, which became my true passion.
<br><br>
More specifically I focused on combining deep neural networks with structured prediction. During my doctoral studies, I also acted as a researcher at the R&D institute <a href="http://www.imec-int.com/en/articles/smart-mobility" target="_blank">imec</a>, which allowed me to apply my research to visual perception in <a href="http://media.cnhindustrial.com/EMEA/CNH-INDUSTRIAL-CORPORATE/cnh-industrial-brands-reveal-concept-autonomous-tractor-development--driverless-technology-to-boost-/s/a2259742-061a-412a-8a12-d307dbaedd88" target="_blank">autonomous agricultural vehicles</a>, in collaboration with <a href="http://www.cnhindustrial.com" target="_blank">CNH Industrial</a>.
<br><br>
Later as part of my doctoral studies, I investigated exploration strategies for deep reinforcement learning. During this time, I pursued research both at <a href="https://openai.com/blog/generative-models/" target="_blank">OpenAI</a> and at the <a href="http://bair.berkeley.edu/" target="_blank">Berkeley Artificial Intelligence Research lab (BAIR)</a>, part of <a href="https://eecs.berkeley.edu" target="_blank">UC Berkeley</a>, with <a href="https://people.eecs.berkeley.edu/~pabbeel/" target="_blank">Pieter Abbeel</a>. This latter stay was supported by a Travel Grant of the FWO.
<br><br>
From 2017 to 2018, I was a research scientist at OpenAI in machine learning with a focus on deep reinforcement learning. I co-organized the Deep Reinforcement Learning Workshop at NIPS <a target="_blank" href="https://sites.google.com/view/deeprl-symposium-nips2017">2017</a>/<a target="_blank" href="https://sites.google.com/view/deep-rl-workshop-nips-2018">2018</a> and was involved in the Berkeley <a target="_blank" href="https://sites.google.com/view/deep-rl-bootcamp/lectures">Deep RL Bootcamp</a>.
<br><br>
In 2018 I co-founded the San Francisco/Beijing AI lab at <a target="_blank" href="http://www.happyelements.com/ai/detail/">Happy Elements</a> where I am currently Head of AI. Our team builds machine learning algorithms and their supporting infrastructure and analytics platforms for online gameplay generation in mobile games, servicing over 100 million unique players each month. A presentation given at the <a href="https://www.re-work.co" target="_blank">RE-WORK</a> Applied AI Summit can be found <a href="pdf/rework.pdf#zoom=100" target="_blank">here</a> which explains on a high level how our team is applying deep (reinforcement) learning for hyper-personalization.
</p>
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<a href="cv.pdf#zoom=100" target="_blank" class="btn btn-default btn-lg"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">Curriculum Vitae</span></a><!--<a href="cv.pdf#zoom=100" target="_blank" class="btn btn-default btn-lg"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">Curriculum Vitae</span></a>-->
</li>
</ul>
</section>
<section id="projects" class="content-section text-center">
<div class="container">
<div class="col-lg-8 col-lg-offset-2 text-left">
<h2 class="text-center">Selected Projects</h2>
<br><br>
<h3>
Meta-learning
</h3>
<p>
Learning to learn in deep reinforcement learning (RL), including learning to explore without the use of additional structures. Below a video of a robot learning to grasp from scratch without simulator resets and a robot learning to walk towards targets out of training distribution, using Evolved Policy Gradients (EPG).
</p>
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<div class="wrapper" style="background-color: rgba(255, 255, 255, 0.0);">
<div style="float: left; width: 44.5%; background-color: rgba(255, 255, 255, 0.0);">
<video controls="controls" autoplay="autoplay" loop="loop" onstart="this.play();" onended="this.play();" width="100%"><source src="https://storage.googleapis.com/epg-blog-data/fetch_epg_3.mp4" type="video/mp4"></video>
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<video controls="controls" autoplay="autoplay" loop="loop" onstart="this.play();" onended="this.play();" width="100%" style=""><source src="https://storage.googleapis.com/epg-blog-data/ant_rl2_4.mp4" type="video/mp4"></video>
</div>
<div id="clear" style="clear:both;"></div>
</div>
<br><br>
<!-- BEGIN: EPG -->
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/epg.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1802.04821">Evolved Policy Gradients</a><br>Advances in Neural Information Processing Systems (NeurIPS), 2018<br>
<small class="authors">
R. Houthooft, R. Y. Chen, P. Isola, B. C. Stadie, F. Wolski, J. Ho, P. Abbeel
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/epg.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1802.04821.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://github.com/openai/EPG" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-github fa-fw"></i> <span class="network-name">github</span></a>
</li>
<li>
<a href="https://blog.openai.com/evolved-policy-gradients/" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-link fa-fw"></i> <span class="network-name">blog</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/exploration-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1611.04717v2.pdf">pdf</a></small>-->
</th>
</tr>
</table>
<!-- END: EPG -->
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/emaml.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1803.01118">Some Considerations on Learning to Explore via Meta-Reinforcement Learning</a><br>Advances in Neural Information Processing Systems (NeurIPS), 2018<br>
<small class="authors">
B. C. Stadie, G. Yang, R. Houthooft, X. Chen, Y. Duan, W. Yuhuai, P. Abbeel, I. Sutskever
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/emaml.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1803.01118.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/exploration-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1611.04717v2.pdf">pdf</a></small>-->
</th>
</tr>
</table>
<br><br><br><br>
<h3>
Deep Reinforcement Learning
</h3>
<p class="text-justify">
Reinforcement learning (RL) using nonlinear function approximators with a focus on continuous control tasks such as robot locomotion. In particular, the goal is to investigate how to achieve efficient exploration in deep RL through curiosity. This research was performed in collaboration with OpenAI and the Berkeley AI Research lab.
</p>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/noise.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1706.01905">Parameter Space Noise for Exploration</a><br>International Conference on Learning Representations (ICLR), 2018<br>
<small class="authors">
M. Plappert, R. Houthooft, P. Dhariwal, S. Sidor, R. Y. Chen, X. Chen, T. Asfour, P. Abbeel, M. Andrychowicz
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/noise-17.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1706.01905.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://blog.openai.com/better-exploration-with-parameter-noise/" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-link fa-fw"></i> <span class="network-name">blog</span></a>
</li>
<li>
<a href="https://vimeo.com/252185862" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-video-camera fa-fw"></i> <span class="network-name">video</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/exploration-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1611.04717v2.pdf">pdf</a></small>-->
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/atari.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1611.04717">#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning</a><br>Advances in Neural Information Processing Systems (NIPS), 2017<br>
<small class="authors">
H. Tang, R. Houthooft, D. Foote, A. Stooke, X. Chen, Y. Duan, J. Schulman, F. De Turck, P. Abbeel
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/exploration-16.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1611.04717v2.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://www.youtube.com/playlist?list=PLAd-UMX6FkBQdLNWtY8nH1-pzYJA_1T55
" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-video-camera fa-fw"></i> <span class="network-name">video</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/exploration-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1611.04717v2.pdf">pdf</a></small>-->
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/bnn.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1605.09674/">VIME: Variational Information Maximizing Exploration</a><br>Advances in Neural Information Processing Systems (NIPS), 2016 <br>
<small class="authors">
R. Houthooft, X. Chen, Y. Duan, J. Schulman, F. De Turck, P. Abbeel
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/vime-nips-16.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1605.09674.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://github.com/openai/vime" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-github fa-fw"></i> <span class="network-name">github</span></a>
</li>
<li>
<a href="https://openai.com/blog/generative-models/" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-link fa-fw"></i> <span class="network-name">blog</span></a>
</li>
<li>
<a href="https://youtu.be/nbbMSMv3v5k" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-video-camera fa-fw"></i> <span class="network-name">video</span></a>
</li>
</ul>
<!--<small>
<a href="bibtex/vime-nips-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1605.09674v3.pdf">pdf</a> — <a href="https://github.com/openai/vime">github</a> — <a href="https://openai.com/blog/generative-models/"> blog post </a> — <a href="https://youtu.be/nbbMSMv3v5k">spotlight video</a>
</small>-->
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/rllab.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="http://arxiv.org/abs/1604.06778/">Benchmarking Deep Reinforcement Learning for Continuous Control</a><br>International Conference on Machine Learning (ICML), 2016<br>
<small class="authors">
Y. Duan, X. Chen, R. Houthooft, J. Schulman, P. Abbeel
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/icml-16.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1604.06778.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://github.com/rllab/rllab" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-github fa-fw"></i> <span class="network-name">github</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/icml-16.txt">bibtex</a> — <a href="https://arxiv.org/pdf/1604.06778.pdf">pdf</a> — <a href="https://github.com/rllab/rllab">github</a>
</small>
-->
</th>
</tr>
</table>
<br><br><br><br><h3>
Generative Models
</h3>
<p class="text-justify">
InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound of the mutual information objective that can be optimized efficiently.
</p>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/infogan.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://arxiv.org/abs/1606.03657">InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets</a><br>Advances in Neural Information Processing Systems (NIPS), 2016<br>
<small class="authors">
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/infogan-nips-16.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="https://arxiv.org/pdf/1606.03657v1.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="https://github.com/openai/InfoGAN" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-github fa-fw"></i> <span class="network-name">github</span></a>
</li>
<li>
<a href="https://openai.com/blog/generative-models/" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-link fa-fw"></i> <span class="network-name">blog</span></a>
</li>
</ul>
</th>
</tr>
</table>
<br><br><br><br>
<h3>
Structured Prediction and Deep Learning
</h3>
<p class="text-justify">
As part of an <a href="http://media.cnhindustrial.com/EMEA/CNH-INDUSTRIAL-CORPORATE/cnh-industrial-brands-reveal-concept-autonomous-tractor-development--driverless-technology-to-boost-/s/a2259742-061a-412a-8a12-d307dbaedd88" target="_blank">autonomous vehicle</a> project, the goal was to combine structured output prediction and deep learning techniques, with a particular focus on semantic image segmentation. Structural support vector machines (SSVMs) were extended to allow for highly nonlinear factors. This can enhance output coherence of deep predictive models, while still allowing for end-to-end training. Below the architecture of a deep SSVM with convolutional neural factors is pictured.
<br><br>
<img width="100%" src="img/thumbnails/deep_ssvm.png""/>
<br>
</p>
<table class="entry">
<tr>
<th>
<img width="170" src="img/thumbnails/nf.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="http://www.sciencedirect.com/science/article/pii/S0031320316001096">Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines</a><sup>*</sup><br>Pattern Recognition, vol. 59, 2016<br>
<small class="authors">
R. Houthooft, F. De Turck
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/pr.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="http://arxiv.org/pdf/1508.00451v4.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="pdf/addendum.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">addendum</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/pr.txt">bibtex</a> — <a href="http://arxiv.org/pdf/1508.00451v4.pdf">pdf</a>
</small>
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 20px" src="img/thumbnails/aaai.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="">Structured Output Prediction for Semantic Perception in Autonomous Vehicles</a><sup>*</sup><br>The 30th AAAI Conference on Artificial Intelligence (AAAI), 2016<br>
<small class="authors">
R. Houthooft, C. De Boom, S. Verstichel, F. Ongenae, F. De Turck
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/aaai-16.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="pdf/Houthooft2016_AAAI.pdf"" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
<li>
<a href="pdf/addendum.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">addendum</span></a>
</li>
<small>
<a href="bibtex/aaai-16.txt">bibtex</a> — <a href="pdf/Houthooft2016_AAAI.pdf">pdf</a>
</small>
-->
</th>
</tr>
</table>
<p class="text-justify">
<sup>*</sup> This work is part of an applied research project in collaboration with <a href="http://www.cnhindustrial.com" target="_blank">Case New Holland (CNH) Industrial</a>. As such several methods, models, datasets, and results could not be publicly released due to confidentiality agreements. An addendum to these papers can be found <a href="pdf/addendum.pdf" target="_blank">here</a>. Initial vehicle controller <a href="http://www.google.com/patents/US20170006261">patent applications</a> have been filed.
</p>
<br><br><br><br>
<!--<h3>
Artificial Intelligence in Healthcare
</h3>
<p class="text-justify">
In collaboration with the <a href="http://www.ugent.be/ge/inwgen/nl/onderzoek/intensieve">Laboratory of Intensive Care Outcomes Research</a> of the <a href="http://www.uzgent.be">Universitair Ziekenhuis Gent</a>, this applied research project investigates the use of machine learning techniques for predictive modeling and medical decision support in the intensive care unit. </p>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 80px" src="img/thumbnails/rf.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="https://www.hindawi.com/journals/cmmm/2016/7087053/abs/">Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit</a><br>Computational and Mathematical Methods in Medicine, 2016<br>
<small class="authors">
J. Ruyssinck, J. van der Herten, R. Houthooft, F. Ongenae, I. Couckuyt, B. Gadeyne, K. Colpaert, J. Decruyenaere, F. De Turck, T. Dhaene</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/cm" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="http://downloads.hindawi.com/journals/cmmm/2016/7087053.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<small>
<a href="bibtex/cm">bibtex</a> — <a href="http://downloads.hindawi.com/journals/cmmm/2016/7087053.pdf">pdf</a>
</small>
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 80px" src="img/thumbnails/aiim.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="http://dx.doi.org/10.1016/j.artmed.2014.12.009">Predictive Modelling of Survival and Length of Stay in Critically Ill Patients using Sequential Organ Failure Scores</a><br>Artificial Intelligence in Medicine, vol. 63, no. 3, 2015<br>
<small class="authors">
R. Houthooft, J. Ruyssinck, J. van der Herten, S. Stijven, I. Couckuyt, B. Gadeyne, F. Ongenae, K. Colpaert, J. Decruyenaere, T. Dhaene, F. De Turck
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/aiim.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="pdf/Houthooft2015_AIIM.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<small>
<a href="bibtex/aiim.txt">bibtex</a> — <a href="pdf/Houthooft2015_AIIM.pdf">pdf</a>
</small>
</th>
</tr>
</table>
<br><br><br><br>-->
<h3>
Network Science
</h3>
<p class="text-justify">
My research originally focused on the development of a novel routing algorithm called <i> Forest Routing</i>. Through geometric routing, using a set of graph embeddings in a particular mathematical space, it offers both high scalability and native load balancing behavior. A coherent write-up on the subject can be found in my thesis <a href="pdf/Houthooft_MScThesis.pdf">Adaptive Geometric Routing for the Internet Backbone</a>. Below a demonstration of the developed model is shown.
</p>
<div class="wrapper">
<!-- <div class="h_iframe">
<iframe height="2" width="2" src="https://www.youtube.com/embed/7SV0635cR6w?theme=light&showinfo=0&showsearch=0&rel=1&color=white&vq=hd720&loop=1" frameborder="0" allowfullscreen></iframe>
</div> -->
<video controls="controls" autoplay="autoplay" loop="loop" onstart="this.play();" onended="this.play();" width="100%"><source src="vid/forest_routing_demo.mp4" type="video/mp4"></video>
</div>
<br><br>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 20px" src="img/thumbnails/networks.png" class="rein-paper"/>
</th>
<th class="entry">
<a>Optimizing Robustness in Geometric Routing via
Embedding Redundancy and Regeneration</a><br>Networks, vol. 66, no. 4, 2015
<br>
<small class="authors">
R. Houthooft, S. Sahhaf, W. Tavernier, F. De Turck, D. Colle, M. Pickavet
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/networks.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="pdf/Houthooft2015_Networks.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/networks.txt">bibtex</a> — <a href="pdf/Houthooft2015_Networks.pdf">pdf</a>
</small>
-->
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 20px" src="img/thumbnails/infocom.png" class="rein-paper"/>
</th>
<th class="entry">
<a>Robust Geometric Forest Routing with Tunable Load Balancing</a><br>The 34th Annual IEEE International Conference on Computer Communications (INFOCOM), 2015
<br>
<small class="authors">
R. Houthooft, S. Sahhaf, W. Tavernier, F. De Turck, D. Colle, M. Pickavet
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/infocom.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="pdf/Houthooft2015_INFOCOM.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/infocom.txt">bibtex</a> — <a href="pdf/Houthooft2015_INFOCOM.pdf">pdf</a>
</small>
-->
</th>
</tr>
</table>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 60px" src="img/thumbnails/rndm.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="http://dx.doi.org/10.1109/RNDM.2014.7014924">Fault-Tolerant Greedy Forest Routing for Complex Networks</a><br>The 6th International Workshop on Reliable Networks Design and Modeling (RNDM), 2014 — <span class="rein_green">best paper award</span>
<br>
Featured in <a href="pdf/gcn.pdf">Global Communications Newsletter</a> May 2015
<br>
<small class="authors">
R. Houthooft, S. Sahhaf, W. Tavernier, F. De Turck, D. Colle, M. Pickavet
</small>
<br>
<ul class="list-inline list-inline-x text-left">
<li>
<a href="bibtex/rndm.txt" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-text-o fa-fw"></i> <span class="network-name">bibtex</span></a>
</li>
<li>
<a href="pdf/Houthooft2014_RNDM.pdf" target="_blank" class="btn-x btn-default-x btn-lg-x"><i class="fa fa-file-pdf-o fa-fw"></i> <span class="network-name">pdf</span></a>
</li>
</ul>
<!--
<small>
<a href="bibtex/rndm.txt">bibtex</a> — <a href="pdf/Houthooft2014_RNDM.pdf">pdf</a>
</small>
-->
</th>
</tr>
</table>
<!--
<img height="100" src="pdf/Houthooft_MScThesis.png" class="rein-paper"/>
<p class="entry">
<a href="http://lib.ugent.be/fulltxt/RUG01/002/153/612/RUG01-002153612_2014_0001_AC.pdf">Adaptive Geometric Routing for the Internet Backbone</a><br>Master's thesis, 2014<br>
<small class="authors">
R. Houthooft
</small>
<br>
<a href="bibtex/msc.txt">bibtex</a> — <a href="pdf/Houthooft_MScThesis.pdf">pdf</a>
</p>--><br>
<br><br>
<!--<h3>
Security
</h3>
<p class="text-justify">
Student project in the domain of security for Web services as part of a distributed systems course.
</p>
<table class="entry">
<tr>
<th>
<img width="170" style="margin-bottom: 80px" src="img/thumbnails/noms.png" class="rein-paper"/>
</th>
<th class="entry">
<a href="http://dx.doi.org/10.1109/NOMS.2014.6838308">Design of a Security Mechanism for RESTful Web Service Communication Through Mobile Clients</a><br>IEEE Network Operations and Management Symposium (NOMS), 2014<br>
<small class="authors">
F. De Backere, B. Hanssens, R. Heynssens, R. Houthooft, A. Zuliani, S. Verstichel, B. Dhoedt, F. De Turck
</small>
<small>
<br><a href="bibtex/noms.txt">bibtex</a> — <a href="pdf/DeBackere2014_NOMS.pdf">pdf</a>
</small>
</th>
</tr>
</table>
<br>
<br><br><br>-->
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<li>
<a href="https://scholar.google.com/citations?user=HBztuGIAAAAJ" class="btn btn-default btn-lg"><i class="fa fa-google fa-fw"></i> <span class="network-name">Google Scholar</span></a>
</li>
<!--<li>
<a href="https://www.researchgate.net/profile/Rein_Houthooft" class="btn btn-default btn-lg"><i class="fa fa-flask fa-fw"></i> <span class="network-name">ResearchGate</span></a>
</li>-->
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<h2>Contact</h2>
<p>Feel free to contact me through any of the below.</p>
<p><a href="mailto:[email protected]">[email protected]</a>
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