Designed and evaluated world-model architectures for spatial reasoning, improving long-horizon prediction accuracy by ~18–25% on simulated navigation and embodied AI benchmarks.

Led experiments across 10k–100k+ simulated trajectories, developing data pipelines and evaluation metrics that reduced training instability and cut experiment iteration time by ~30%.

Implemented relational spatial reasoning modules inspired by quantum entanglement to model object-to-object and scene-level constraints, reducing combinatorial search overhead; collaborated within a 5–8 person research team on reusable research code and publications.

Contributed to spacecraft trajectory and propulsion simulations supporting Orion’s deep-space navigation planning and GN&C systems.

Built predictive models for flight stability and anomaly detection, improving simulation reliability by 14%.

Streamlined research workflows across cross-functional teams, reducing analysis cycle time by 17%.

Mentoring and leading AI/ML workshops for undergraduates

Organizing hands-on sessions on deep learning and classical mathematics