Welcome! I'm excited to start sharing my thoughts, research notes, and practical insights in this space.
This blog will primarily cover:
- Efficient deep learning and compression methods — pruning, quantization, distillation, and structured sparsity techniques that make neural networks smaller and faster without sacrificing performance.
- Practical lessons from industry ML deployment — real-world challenges and solutions from deploying models at companies like Zeiss, BMW, Huawei, and Intel.
- Research notes on LLM optimization and sparsity — deep dives into recent papers, including my own work on Bayesian sparsification published at NeurIPS.
I'll publish short, practical write-ups and research-oriented posts regularly. The goal is to bridge the gap between academic research and practical engineering — making efficient AI more accessible to everyone.
"The best model is not the biggest one — it's the one that solves the problem within your constraints."
Stay tuned for the first real post coming soon. In the meantime, feel free to check out my projects or publications.
— Rayen