I am an Applied Scientist focused on the intersection of Large Language Models (LLMs) and Reinforcement Learning (RL). I'm passionate about making complex AI concepts accessible and turning AI learning into a fun experience.
I am currently writing AI-101, an open-resource book born from my personal learning notes. It is designed to be an accessible guide for anyone—from curious beginners to fellow practitioners—looking to demystify the mechanics of modern AI.
Latest Updates:
- ✅ Transformers: Deep dive into architectures.
- ✅ RLHF: Explaining theory behind PPO.
- ✅ RLVR: Understanding Reinforcement Learning from Verifiable Rewards.
- 🚧 Evaluation (WIP): Benchmarks, metrics, and frameworks for LLMs and Agent systems.
- 🚧 Responsible AI (WIP): Model safety eval, Moderation, Safeguard LLM.
- 🚧 More chapters in progress—stay tuned!
- Research interests: LLM, Fine-tuning (SFT/RLHF/RLVR), Topic modeling, Evaluation, Inference optimization.
- Tools: PyTorch, Transformers, Unsloth, Smolagents, OpenJudge.