
AI Engineer at b(x) Theory
May 2026 – Aug 2026Built the document intelligence and financial RAG platform behind a restructuring analytics product

Software Engineering University of Waterloo
I'm a Software Engineering student at UWaterloo working across ML research and applied AI. I've published on interpretability and context compression, and I build reinforcement learning agents, including a Minecraft combat bot that beats 99% of human players. Right now I'm co-leading WorldFold at Wat.AI, teaching a dual-arm robot to fold cloth with world models. Open to new opportunities and collaborations.

Built the document intelligence and financial RAG platform behind a restructuring analytics product

Leading WorldFold, a project exploring world models and reinforcement learning for robotic cloth manipulation

Trained statistical and deep learning classifiers to identify market regimes in noisy financial time series

Developed AI agent integrations and improved natural language processing systems
Adam Kamel et al.
NeurIPS 2025 MechInterp Workshop · EMNLP 2025 BlackboxNLP Workshop
Explores whether transformers build internal world models of games they can't fully observe. Generated 3M+ simulated poker games to train a GPT-2-style model on hand data, then probed its internal activations, recovering hand strength with 98% accuracy and showing the model encodes its own win probability (r = 0.59), information it was never shown during training.
Adam Kamel, Eric Xu
KnowFM Workshop @ ACL 2026
A training-free method that compresses long documents into reusable vector representations. Benchmarked against leading compression baselines on QA and code tasks across 7B–70B models, RSCE retains 60–81% of answer quality while using 99% fewer tokens, and the paper identifies why quality drops when it does.

Self-play RL agent that beats 99% of human players in Minecraft sword duels

Chess engine that placed 3rd overall out of 75 teams at ChessHacks