M Tapkan
Machine Learning Engineer Researcher at Fathom
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolM Tapkan is a Machine Learning Engineer and researcher with a decade of experience building production-ready ML systems and research-grade game-playing AI. Based in Vancouver, he has blended academic rigor from an MS at University of Alberta—where his work produced state-of-the-art Dark Hex players—with industry impact at Fathom, developing proprietary LLM frameworks, ethical data pipelines, and tools that increased engineering efficiency by 250%. He contributes to open source (notably adding Phantom Hex to DeepMind’s OpenSpiel) and maintains algorithmic repositories for competitive programming, reflecting deep strengths in algorithms, reinforcement learning, and systems engineering. Comfortable across the stack, he pairs classical ML techniques with modern LLM toolchains and vector-RAG systems to turn research ideas into deployed solutions.
10 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Alberta
English, Turkish
Stackoverflow
28reputation
521reached
2answers
2questions
Github Skills (29)
algorithm10
algorithms10
c-language10
python10
cd10
coregames10
multi-agent10
c1110
data-structure10
implement10
c1710
data-structures10
lcm10
cpp10
game-development10
Programming languages (10)
Github contributions (5)
BedirT/ACM-ICPC-Preparation
May 2016 - Nov 2022
ACM-ICPC Preparation Guide
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 80 commits, 16 PRs in 6 years 7 months
Contributions summary:M contributed code related to fundamental algorithms and data structures, focusing on implementations in C++, Java, and Python. They implemented solutions for common algorithms such as GCD and LCM, including different approaches like Euclidean algorithm variants. The user also worked on prime factorization and problems involving prime number generation and number theory concepts. The contributions suggest a strong understanding of computational thinking and algorithmic problem-solving.
acmpreparationacm-icpcicpc
google-deepmind/open_spiel
Feb 2021 - May 2022
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Role in this project:Back-end Developer
Back-end DeveloperContributions:10 reviews, 41 commits, 13 PRs in 1 year 2 months
Contributions summary:M primarily contributed to the `open_spiel` repository by adding a new game, Phantom Hex, and subsequently updating and fixing existing games, Dark Hex and Phantom TTT. Their work involved implementing game logic, including defining states, legal actions, applying actions, and handling information states. These changes suggest a focus on extending the game library with new game environments and ensuring their proper functioning within the framework.
cppmultiagentgamespythondatamining
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