Research

I'm an AI researcher with a civil engineering background. My work spans 3D perception, agentic AI, and AI safety, and I care most about research that leaves the lab and holds up in the real world. I like to build end to end, from field sensing systems to the learning algorithms behind them.

Research Directions

3D reconstruction

013D (Gaussian Splatting, LiDAR)

Real-time 3D reconstruction from LiDAR and cameras: LiDAR-inertial odometry, mobile mapping, and neural representations such as NeRF and 3D Gaussian Splatting. I shipped these ideas as DepthViz, an on-device iOS LiDAR-SLAM scanner, and study lightweight odometry and open-vocabulary 3D scene understanding for robots.

Agentic AI

02Agentic AI

Agents that act reliably in the real world: mobile GUI agents, multi-agent orchestration, and on-device collaborative behavior. My work on MATE (Mobile Agent Trustworthy Execution) makes agent actions safe and efficient, and I bring the same ideas to ontology-based home robots and multi-user memory control.

AI safety

03AI Safety

Making AI systems trustworthy: safety evaluation for multimodal generative AI, robust detection of AI-generated images and video, and efficient, verifiable inference. This line runs from agent-level safety to content-level trust, connecting my work on generative-content detection and reliable execution.


Real-World Problems

More than any single method, this is the area I care about most: research that leaves the lab and solves an actual problem in the field.

Real-world remote sensing

Detection-driven super-resolution for sonar imagery (IEEE TGRS) for mine-like object detection, UAV SAR and LiDAR digital twins for landslide and disaster mapping, and remote sensing for environmental monitoring. I want work where the benchmark is the real world, not just a dataset.

Funded Research Projects

See the full Publications list for papers.