Intelligent Enterprise Operations
Coordinating, securing, and learning across distributed autonomous operations
What it means
AI, optimization, and system-design methods that coordinate resources, robots, workflows, facilities, and operational decisions across complex manufacturing environments.
Why it matters
AMR deployments rely on manually specified routes, rule-based dispatching, and pre-set workflows that cannot adapt when tasks, layouts, or priorities change. Multi-site enterprises need visibility across distributed operations without centralizing proprietary data.
What this research enables
Value to Industry
- Higher throughput and better utilization across distributed operations
- Protection of production systems and proprietary data
- Faster discovery through autonomous experimentation
Autonomous workflows for materials discovery
Coordinating fleets of mobile robots for adaptive material movement
Operational Gap
Core Innovation
Value to Industry
Autonomous Multi-Robot Orchestration
Coordinating fleets of mobile robots for adaptive material movement
Operational Gap
Core Innovation
Value to Industry
Dynamic task allocation and routing for AMR fleets
Faculty:
Battery-, workload-, and condition-aware dispatching
Faculty: Jianjun Shi
Reliable Physical AI for Manufacturing
Autonomy that holds up outside the conditions it was built for
Operational Gap
Autonomy systems perform well under predefined conditions but degrade in new facilities, unfamiliar layouts, degraded sensing, ambiguous instructions, and out-of-distribution scenarios
Core Innovation
Robot intelligence that generalizes across facilities rather than being reconfigured for each one, detecting its own failures, recovering from them, and explaining its decisions to human supervisors
Value to Industry
Robot deployment that scales across facilities, less manual configuration, autonomy failures caught before they become costly, and robot decisions a supervisor can understand and trust
Robust navigation across facility layouts
Faculty: Jianjun Shi
U.S. National Science Foundation (NSF)Vision-language scene understanding in the facility
Faculty: Jianjun Shi
U.S. National Science Foundation (NSF)Secure Distributed Operations
Using causal discovery to detect abnormal process relationships across enterprise sites without centralizing sensitive data
Operational Gap
Distributed manufacturers lack visibility into process relationships across plants. Most monitoring remains site-local and signal-level; subtle disruptions only appear as changes in process relationships.
Core Innovation
Federated causal AI detects temporal relationships among process variables across distributed sites and enables identification of abnormal causal patterns linked to cyberattacks, process disruptions, or degraded operations.
Value to Industry
Improves enterprise-wide visibility without centralizing sensitive plant data, supports root-cause investigation, and strengthens resilience across distributed operations.
Cyberattack detection across multi-site manufacturing systems
Faculty: Nagi Gebraeel
Novelis U.S. National Science Foundation (NSF)Work With Us
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