Max Lapan
Gronau, North Rhine-Westphalia, Germany
Summary
🤩
Rockstar🎓
Top SchoolMax Lapan is an experienced machine learning and systems engineer with 18 years building high-throughput, 24/7 production systems and scalable ML platforms from Hadoop/HBase clusters to modern LLM and RL applications. He is the author of the Deep Reinforcement Learning Hands-On book (three editions) and contributes practical example code and fixes to its repositories, bridging academic algorithms and production-ready implementations. Max has led ML teams and data pipelines at companies like scoutbee and Coins.ph, shipping NLP and fraud-detection models at scale, and has a strong background in performance optimization, HPC and storage subsystems from roles at Mail.Ru and Yandex. An active open-source contributor, he also works on embedded IoT firmware (notably Flipper Zero sub-GHz/NFC decoders), highlighting a rare full-stack fluency from kernel and cloud to reinforcement learning and LLMs.
18 years of coding experience
13 years of employment as a software developer
Stanford Online Cources on ML, AI and DB.Master's degree., CS and software development, Master's degree., CS and software development at Rybinsk State Aviation Technological Academy named after P. A. Solovyov
English, Russian, Dutch, German
Stackoverflow
21reputation
682reached
0answers
1question
Github Skills (19)
pytorch10
python10
nfc10
firmware10
machine-learning10
c1110
c1710
tensorboard10
ignite10
deep-reinforcement-learning10
stm10
su10
rfid9
armv79
jupyter-notebook8
Programming languages (15)
Github contributions (5)
PacktPublishing/Deep-Reinforcement-Learning-Hands-On
May 2018 - May 2020
Hands-on Deep Reinforcement Learning, published by Packt
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 11 PRs, 38 pushes in 2 years
Contributions summary:Max primarily contributed to the reinforcement learning implementations within the repository, fixing issues and making improvements to existing code. This involved addressing compatibility issues with PyTorch, correcting indexing errors in D4PG implementations, and fixing a KL calculation error. The user also made adjustments to the training process and related files, including image color schemes and test frequency settings, indicating a focus on improving model training and evaluation.
hands-ondeep-learninghandsreinforcement-learningpackt
PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition
Jul 2019 - Jan 2022
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
Role in this project:ML Engineer
ML EngineerContributions:278 commits, 9 PRs, 270 pushes in 2 years 6 months
Contributions summary:Max's commits focus on integrating and porting code from a previous edition of a deep reinforcement learning book. The changes include importing and adapting example code, especially related to a tournament environment within Chapter 18. The user also removed tensorboardX and adapted code to work with the Ignite framework, indicating a focus on the practical application and presentation of the educational material around deep reinforcement learning algorithms.
hands-ondeep-learninghandsreinforcement-learningpackt
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