Yuxuan Zhang
Applied AI Scientist at GovTech Singapore
Singapore
Summary
🤩
Rockstar🎓
Top SchoolYuxuan Zhang is an Applied AI Scientist based in Singapore with 4 years of experience delivering production-ready AI and data solutions across public and private sectors. He specializes in generative AI—building RAG pipelines and LLM chatbots—and has hands-on experience adapting state-of-the-art diffusion and text-to-video models through contributions to Hugging Face’s diffusers repository. Previously at QuantumBlack/McKinsey and SWAT Mobility he translated ambiguous business needs into scalable AI products and optimized data-driven mobility systems across major Asian cities. His work in responsible AI at Swiss Re contributed to MAS-published research, reflecting a strong grounding in fairness and governance alongside engineering. Yuxuan combines practical deployment experience with open-source collaboration to make advanced AI accessible to organizations of varying sizes.
4 years of coding experience
1 year of employment as a software developer
Bachelor of Engineering (B.Eng.), Bachelor of Engineering (B.Eng.) at Nanyang Technological University SingaporeMaster of Science - MS, Business Analytics, Master of Science - MS, Business Analytics at NUS Masters of Science in Business Analytics (MSBA)
Chinese, English
Github Skills (12)
transformers10
diffusion-models10
pytorch10
machine-learning10
deeplearning-ai10
diffusion-probabilistic10
deep-learning10
diffusers10
diffusion-probabilistic-models10
flax9
jax9
stable-diffusion8
Programming languages (5)
Github contributions (5)
huggingface/diffusers
Jul 2024 - Mar 2025
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Role in this project:
ML Engineer
ML EngineerContributions:30 reviews, 10 PRs, 52 comments in 8 months
Contributions summary:Yuxuan contributed significantly to the `huggingface/diffusers` repository, which focuses on diffusion models. The commits demonstrate the user's involvement in adding and adapting CogVideoX models for text-to-video generation. This includes integrating the model, modifying existing code for compatibility, and preparing the pipeline to work with different configurations. The contributions involve model adaptation and pipeline enhancements within the context of image-to-video generation.
pytorchartdeep-learningimage2imagestate-of-the-art
THUDM/CogView4
Sep 2024 - Mar 2025
Contributions:4 reviews, 31 PRs, 49 pushes in 6 months
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