MSc Computer Science (Thesis) at Toronto Metropolitan University
I build intelligent systems across world models, model-based reinforcement learning, state space models, and LLM applications. Industry experience includes end-to-end ML deployment at CubeHx/CubeGo and LLM research at TMU.
About
Thesis-driven machine learning research with practical deployment experience.
World models, model-based reinforcement learning, and state space models for robust long-horizon prediction and planning.
NSERC CGS-M ($27,500), Toronto Metropolitan Graduate Fellowship ($9,000), and NSERC USRA ($6,000).
Published in Knowledge and Information Systems and Social Network Analysis and Mining, with an additional pre-print on political-news recommendation.
Built and deployed end-to-end ML systems across computer vision, predictive modeling, and LLM-driven research workflows.
Work
Selected projects from my resume focused on world modeling, LLM robustness, and retrieval-augmented generation.
Implemented Mamba-2 in JAX with vector quantization for efficient sequence modeling and stronger long-horizon prediction.
Built a reproducible training and benchmarking pipeline for BERT and RoBERTa over 50K+ samples with robustness evaluation.