Research at the intersection of AI, systems, and applied learning.

Designing intelligent architectures that govern critical system resources, whether those resources are computational capacity, data, or learning experience, to improve the reliability, efficiency, and trustworthiness of AI systems.

ADSL

Adaptive decision systems work exploring feedback loops, policy boundaries, and research structures that can become reliable software behavior.

  • Decision boundaries and feedback loops.
  • Evaluation-friendly system design.
  • Clear separation between policy, data, and execution.
Repo

Hardware-Aware Dynamic Resource Allocation

Scheduling research that considers real compute constraints: capacity, contention, locality, changing demand, and workload-specific placement needs.

  • Hardware-aware scoring and placement.
  • Policy separated from resource discovery.
  • Extensible strategies for changing workloads.
Repo