Human-Machine Intelligent Systems Lab

Projects

  • Data Hiding in 3D Meshes Active

    Honors research on steganography for three-dimensional geometry, embedding hidden information in polygonal mesh models so that the payload survives ordinary handling of the model while leaving its visible shape unchanged. The work examines where capacity can be found in a mesh — vertex coordinates, connectivity, and traversal order — and how embedding trades off against distortion and robustness. Conducted in the Honors thesis sequence (CS491). This work began in 2025.

  • Facilitating Rapid Emergency Response via Graph Optimization Algorithms Active

    Research applying graph pathfinding algorithms and network flow analysis to GIS data to optimize emergency fire response. Given an incident location, the system models the road network as a graph and recommends a complete response plan: which fire hydrant to draw from (scored by hose-run length, route curvature, and road grade via shortest-path search), how to route the hose to the scene, and which roads to close — using a minimum-cut computation to find the least-disruptive set of closures that still separates traffic from the incident while protecting crews and equipment. It then places MUTCD-compliant advance warning signs and channelizing-device tapers so the traffic-control plan matches what crews are trained to deploy, all while respecting the limited barricades, signs, and devices available in the field. The tool is built as a Python (FastAPI) planner over the standard geospatial and graph stack (GeoPandas, Shapely, NetworkX, OSMnx) with an interactive map front end.