Maya Chen is an associate professor of computer science whose work examines how learning-enabled systems can be evaluated before they are deployed in settings where mistakes are costly. Her research sits at the intersection of formal methods, machine learning and cyber-physical systems. She is particularly interested in the gap between a model that performs well on a benchmark and a system that behaves reliably when its environment changes.

Chen directs the fictional Reliable Intelligence Laboratory, where graduate and undergraduate researchers develop tools for analyzing autonomous decision making. The group combines mathematical models with carefully designed experiments, using simplified robotic platforms and openly documented simulations to study uncertainty, feedback and failure recovery. A recurring question in her work is how a safety requirement can become a practical test that engineers can reproduce and interpret.

Before joining the faculty, Chen completed doctoral training in computer science and worked as a postdoctoral researcher on verification tools for embedded systems. That experience shaped her collaborative approach: she works closely with researchers in applied mathematics, engineering and human-centered design to make technical guarantees useful to the people who build and operate a system.

In the classroom, Chen connects abstract ideas to small experiments that students can implement themselves. She teaches courses in artificial intelligence and program verification and advises projects that emphasize reproducible results, clear documentation and responsible problem formulation. Her current interests include monitoring deployed models, describing the limits of a verification result and helping students communicate uncertainty without losing precision.