Baiting Luo is a Graduate Research Assistant at Scope Lab developing scalable neuro-symbolic decision procedures that can operate effectively in non-stationary environments. His research combines neural networks and symbolic reasoning to create AI systems that are both powerful and interpretable.
Operating in non-stationary environments means that the rules governing system behavior change over time—a common reality in transportation systems where traffic patterns, infrastructure changes, and user behavior continually evolve. Baiting’s work on neuro-symbolic approaches helps ensure that AI decision systems can adapt to these changing conditions while remaining explainable and trustworthy.