Kai Arulkumaran
Research Scientist at Sakana AI
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolKai Arulkumaran is a research scientist with 13 years of experience bridging deep reinforcement learning research and production-ready tooling, currently based in Chiyoda, Japan. With a PhD in Bioengineering from Imperial College London and a BA in Computer Science from Cambridge, he combines rigorous academic training with hands-on ML engineering—contributions to notable projects include implementing categorical DQN, dueling and noisy layers in the well-known Rainbow RL codebase and improving PyTorch RL and VAE examples. He has led research teams at Araya and held internships at DeepMind, Facebook, and Microsoft, demonstrating a track record of moving ideas from prototype to scalable experiments. Equally fluent in DevOps, his dockerfiles repository shows careful attention to CUDA environments, remote access, and reproducible ML stacks. Outside core research he identifies as a programmer, DJ, and transhumanist, reflecting a curiosity-driven mindset that informs unconventional problem solving.
13 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Bioengineering, Doctor of Philosophy (Ph.D.), Bioengineering at Imperial College LondonBachelor of Arts (BA), Computer Science, Bachelor of Arts (BA), Computer Science at University of Cambridge
Github Skills (17)
pytorch10
docker10
vnc10
variational-autoencoder10
machine-learning10
bash10
dockers10
reinforcement-learning10
autoencoder10
dqn10
deep-learning10
cuda10
deep-reinforcement-learning10
ssh10
linux9
Programming languages (16)
Github contributions (5)
Kaixhin/dockerfiles
Feb 2015 - Aug 2019
Compilation of Dockerfiles with automated builds enabled on the Docker Registry
Role in this project:
DevOps Engineer
DevOps EngineerContributions:362 commits, 6 PRs, 342 pushes in 4 years 5 months
Contributions summary:Kai primarily focused on creating and maintaining Dockerfiles for various CUDA versions and related tools. Their contributions include setting up VNC servers for remote access, configuring SSH access, and integrating ceph scripts. They also implemented environment variable configurations for CUDA libraries and addressed issues related to VNC restarts.
compilationbuildsdockerhydrogenautomated-builds
Kaixhin/Rainbow
Oct 2017 - Nov 2020
Rainbow: Combining Improvements in Deep Reinforcement Learning
Role in this project:ML Engineer
ML EngineerContributions:6 releases, 4 reviews, 145 commits in 3 years 1 month
Contributions summary:Kai primarily contributed to the deep reinforcement learning project by implementing core functionality and integrating new features. Their work involved updating the project's requirements, adding an environment wrapper and implementing a categorical DQN. Furthermore, they addressed the architecture, by adding dueling and noisy layers to the model. These changes reflect an iterative approach to improving the deep reinforcement learning model.
deep-learningreinforcement-learningrainbowdeep-reinforcement-learningimprovements
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