Yike (Eric) Tan

Hi, I’m Yike (Eric) Tan, recently graduated with a Master’s in Artificial Intelligence Engineering from Carnegie Mellon University. I specialize in LLM systems, model optimization, and production ML pipelines, with hands-on experience training large-scale models, deploying RAG systems, and building MLOps infrastructure.

I’ve worked on ML systems across the full lifecycle—from distributed training on Databricks processing TB-scale datasets, to real-time inference services achieving sub-150ms latency with vector databases and optimized model serving. My experience includes LLM fine-tuning, GPU acceleration with custom CUDA kernels, and production MLOps with automated retraining and monitoring.

As a core contributor to Self-LLM (27.9k+ GitHub stars), I’ve helped thousands of developers deploy and optimize LLMs. The project was featured in Google I/O Connect China 2024. I’ve written extensively on model quantization, LoRA fine-tuning, and production deployment best practices. I’ve also published research at IEEE ICASSP 2024 on zero-shot image understanding using multimodal prompting.

I’ve collaborated with Jiangtao Gong from Tsinghua AIR and Sherry Tongshuang Wu from CMU HCI on LLM applications. Check out my publications, GitHub, or connect on LinkedIn.