Martin Andrews is Head of AI and a machine-learning-focused entrepreneur with 14 years of experience building deep learning products and teaching ML across Southeast Asia from his Singapore base. A Cambridge-trained PhD in Machine Learning, he moved from a long quant and structured-products career in New York to refocus on research and production ML, blending financial domain expertise (CDOs, CLOs, structured products) with practical engineering. He leads a stealth-stage DL company while serving as a Google Developer Expert and contributing to open-source ML and graphics projects—refactoring relational-networks in PyTorch and fixing GLSL shader exercises—demonstrating hands-on coding across research and full-stack areas. Known for translating academic ideas into deployable systems, he also advises NLP+KB projects and runs deep learning workshops across the region, combining thought leadership with product delivery.
14 years of coding experience
14 years of employment as a software developer
MA, Mathematics, MA, Mathematics at University of Cambridge
:mortar_board: A workshopper for GLSL shaders and graphics programming
Role in this project:
Full-stack Developer
Contributions:10 commits in 6 days
Contributions summary:Martin contributed to several exercises within the shader-school repository, addressing bugs and implementing improvements related to shader code. The contributions included fixes for issues with circle rendering, initial state initialization in GPGPU examples, and corrections to shader logic. The user also made adjustments to JavaScript code and shader files, suggesting a working knowledge of both frontend and shader languages.
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)
Role in this project:
ML Engineer
Contributions:6 commits, 1 PR, 1 comment in 1 day
Contributions summary:Martin refactored the provided relational network model, introducing a CNN-MLP architecture for comparison. They implemented and debugged Python3 compatibility, ensuring the project's functionality. The user modified data loading and model saving, enhancing the training and evaluation process. The commits also involved refactoring code, improving the overall structure and readability of the project.
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Martin Andrews - Head Of AI at Red Dragon AI Pte Ltd