Hey there! I am Justin, from Belgium. I am a fifth year student at the
University of Edinburgh (UoE) in Scotland dual majoring in
Computer Science and Electrical Engineering. I am currently exploring different fields, and learning Physics and Chess. I also built a profitable SaaS business two profitable SaaS businesses in
my spare time.
I am strong believer of multi-steps plans and that playing the long game is the only way to win big.
I built a SaaS platform for notaries in Belgium. Check out
Easyscale.be if you are not afraid of landing pages written
in French. The product is basically automating a lot of tiresome processes that notaries clerk have to go
through. Me and my partner in crime (who is also my father) decided to build EasyScale after realising that
Excel spreadsheets (??!) are sold for thousands of euros a year to notaries (And they also suck).
I worked at @SizigiStudios as a Research
Sizigi is a tech startup which is building generative models at scale. My intern project,
WaifuLabs.com, got more than 1M users in less than a month.
I researched Simulation to Real which is the process of training robots in simulation (fast and
cheap) and then transferring their learned behaviours to real machines (expensive and slow). I believe
Differentiable Programming is a huge step forward when it comes to accelerating Reinforcement Learning and I have been
investigating building a differentiable rigid body simulator and raytracer.
I built Clearcall with my good friends
Jay Yeung and
The elevator pitch: "Clearcall is an AI platform that puts your customer support on autopilot when your team is sleeping.
We understand the problems of your customers, automate repetitive processes, and collect the information your support agents need to assist them."
Automated Anomaly Detection in Medical Data using Generative Adversarial Networks at Nanyang
Technological University (NTU) in Singapore. Generative Adversarial Networks or GANs are the hot new topic in
machine learning and I wanted to see if I could use GANs to detect anomalies in electroencephalograms (EEG). NTU
gave me a very proprietary dataset of brain readings (EEG) of epileptic patients for me to experiment with. You
can read the complete report here:
I went from knowing almost nothing about Deep Learning to getting a position in AI Research in less than a
I learned a few tricks along the way on how to learn solid background skills and how to not fail miserably at
transitioning from taking tutorials and classes to doing real world research. I'll write about it here soon!
Email me if you want more details or want to get some advice on how to pull that off yourself.
When mode collapsed GANs were driving me too crazy at NTU I started learning Reinforcement Learning
(Checkout the list of papers I read and my notes
here). I quickly got frustrated with the lack of "gluing" frameworks between the good Deep Learning toolkits like
PyTorch and TensorFlow and the good simulation libraries like OpenAI Gym and Mujoco. I thus decided to write
Bezos, a reinforcement learning framework that you can actually understand and extend for your own use cases.