Systems / CPS / Edge AI Research

Dependable and resource-aware ML systems for networked autonomy and constrained deployment

My systems-focused work builds dependable and resource-aware ML systems for networked, resource-constrained platforms (IoT, embedded systems, CPS, and multi-agent autonomy), where decisions must be made under hard constraints on latency, energy, and memory, and where distribution shift and partial observability are common.

Current themes

  • Budget-aware learning and control in autonomy: Through NSF CPS AERIAL (Co-PI), I work on trustworthy and budget-aware learning-and-control for UAV swarm autonomy, along with digital-twin validation protocols and operational reliability metrics.

  • Compact graph inference for deployment: I develop low-rank and constrained training approaches for graph models that make decision-time cost controllable, enabling practical tradeoffs among accuracy, memory, and latency.

  • Efficient inference for large models: I work on inference-time methods such as confidence-guided stopping to reduce compute cost while maintaining task quality, particularly in resource-constrained or budget-limited settings.

  • Protocol-driven evaluation and reliability diagnostics: A recurring goal is to make reliability measurable in operational units (e.g., errors, violations, energy-per-decision) using replayable traces, stress tests, and deployable evaluation pipelines.

Representative applications and settings

  • UAV autonomy and search-and-rescue settings
  • Wireless/networked decision systems
  • Resource-constrained edge/embedded environments