The Glass Lab.
The Glass Lab works on the airspace and the people behind autonomous aviation: integrating unmanned aircraft into the national airspace, the training methods that build aviators, and the STEM workforce that will run it.
Led by Dr. Charles R. Glass, Executive Director, Bailey Military Institute.
The Human Layer of the autonomous airspace.
The autonomous-airspace transition is treated as a technology problem: UTM, detect-and-avoid, new aircraft. The binding constraint is the human. As autonomy rises, the operator's role shifts from flying to supervising, the required competencies change faster than we can teach them, and the pipeline to produce that workforce does not yet exist. The Human Layer is a competency-based science for training, assessing, and certifying the people who will supervise autonomous aircraft and traffic, and the STEM-to-supervisor pipeline that produces them. It anchors to the U.S. DOT's 2025 Advanced Air Mobility workforce mandate.
Open the full view ↗Raise the autonomy level and switch the training model to watch the competency gap open and close. An illustrative model.
↓ White paper · PDFRead onlineTR-2026-18 · survey / position
The operator under observation.
Physical-AI systems sense the workforce in ways a human supervisor cannot. From an ordinary camera and microphone they read operator state (pulse, respiration, fatigue, attention, stress) below the threshold of human sight. The same perception that can protect an operator can expose one. This track studies where the line belongs: what should be sensed, by whom, and with what consent and governance. It is a new competency of the Human Layer.
Modalities the research spans:VideoAudioThermalSpatial RF / mmWave
The Diffusion Layer.
Physical AI runs on silicon, and the world cannot design or make enough of it, because the binding constraint is people. The industry needs on the order of a million more skilled workers by 2030, and the hardest roles are exactly the ones that cannot be taught quickly. This track studies that as a diffusion problem. The knowledge to design chips, drive the tools, and work a fab spreads through a workforce along an adoption curve, and the rate of that curve is a variable we can move. Some of the skill is generic and travels fast; the highest-value skill is esoteric and tacit: it lives in practice and moves slowly, which is why a leading-edge fab is so hard to copy. The Diffusion Layer maps where that line falls, who pays to cross it, whether skill learned on one process node transfers to the next, and whether an AI copilot can carry an expert's tacit knowledge to a novice, compressing the curve that sets how fast the workforce — and the silicon — can scale. It is a new competency of the Human Layer, turned toward the makers.
the-diffusion-layer on GitHub ↗The Silicon for Physical AI course ↗Pull the levers the research identifies — complexity, trialability, cohort imitation, and an AI copilot that diffuses tacit expertise — and watch the adoption curve and the time-to-scale respond, the generic (fast) and esoteric (slow) paths diverging. A Bass/Rogers model; illustrative.
In the field · the science is old and the moment is new — Rogers' diffusion curve and Becker's general-vs-specific human capital meet TinyTapeout's ~$150 path to real silicon and the first evidence that an AI copilot lifts novices most (Generative AI at Work, +34%). The Diffusion Layer treats training itself as the technology to accelerate.
↓ White paper · PDFRead onlineTR-2026-19 · survey / position
What the lab works on.
The system and the workforce, from the airspace to the people who fly it.
Autonomy in the national airspace
Bringing unmanned and autonomous aircraft into the airspace safely: UAS traffic management and the interface with air traffic control.
How aviators are made
Interdisciplinary training methodologies that build aviation and STEM skill, from FAA Part 107 upward.
Recruit, retain, graduate
Pathways that move students into aviation and STEM careers: airport management, air traffic control, and unmanned aircraft.
Charles R. Glass, EdD
Executive Director · Bailey Military Institute
Dr. Charles R. Glass is the Executive Director of Bailey Military Institute, an Eagle Scout, former Navy officer, Eastern Airlines pilot, and commercial drone pilot, and a professional aviator and educator. Dr. Glass's funded research, backed by the Office of Naval Research, the U.S. Coast Guard, the Texas Department of Transportation, the Transportation Research Board, and the U.S. Department of Education, develops strategies to recruit, retain, and graduate students into STEM, and builds workforce pathways across airport management, air traffic control, and unmanned aircraft.
Work with the lab.
Open positions are listed on Careers.