Current themes
3D representation learning, cross-source place recognition, and generative modeling for spatial data.
3D Vision + LiDAR + Generative AI
Building learning systems for spatial understanding across point clouds, viewpoints, and sensing domains.
I am a Ph.D. candidate at the University of Maryland working at the intersection of 3D computer vision, LiDAR perception, and generative models. My research focuses on representation learning for point clouds, cross-source place recognition, and generative approaches that strengthen scene understanding in robotics and digital twin settings.
Research Focus
My work sits in the space between spatial perception, representation learning, and practical systems for 3D data.
3D perception and point clouds
Designing models that better encode point cloud structure, geometric context, and cross-view relationships for robust scene understanding.
- LiDAR
- 3D vision
- Spatial reasoning
Cross-source place recognition
Studying how aerial and ground observations can be aligned despite viewpoint, resolution, and sensing differences in cross-source retrieval tasks.
- Aerial-ground matching
- Localization
- Domain shift
Generative models for 3D data
Exploring latent generative approaches, including diffusion and autoencoder-based methods, for representation learning and scene synthesis.
- Diffusion models
- Autoencoders
- Representation learning
Publications and Current Work
Accepted work, active submissions, and projects currently moving toward publication.
CrossLoc3D
Contributor to a framework for aerial-ground cross-source 3D place recognition, aimed at matching scenes across substantial viewpoint and modality gaps. The project combines 3D representation learning with retrieval-oriented evaluation for challenging localization scenarios.
Current submission
I currently have a research submission under review for IROS 2026 that extends my work in 3D perception, LiDAR-centered scene understanding, and learning-based spatial representations.
- Current status: under review for IROS 2026.
- Theme: 3D vision, point clouds, and robust spatial understanding.
Next manuscript in progress
I am also developing another project toward a CoRL 2026 submission, continuing my interest in robotics-facing 3D learning problems and generative approaches for spatial reasoning.
- Current status: manuscript and experiments in progress for CoRL 2026.
- Positioning: complementary to my ongoing work in perception and generative modeling.
Projects and Software
Selected technical work spanning research prototypes, systems work, and scientific computing.
Sand Browser and spatial index demos
Led development of interactive spatial data structure visualizations and supporting browser-based tooling for understanding indexing and query behavior.
Open demoJAX traffic simulation
Built a differentiable agent-based traffic simulation workflow in JAX to support accelerated computation and optimization-oriented experimentation.
CrossLoc3D
Worked on a diffusion-oriented 3D place recognition system focused on aerial-ground matching and robust retrieval across cross-source data.
Project repositoryExperience
A mix of research, engineering, and applied systems work across academic and government settings.
Artificial Intelligence Researcher, ARLIS
University of Maryland
- Designing 3D spatial software tools for digital twin applications in defense and intelligence contexts.
- Previously worked on reinforcement learning methods for supply chain simulation in AnyLogic.
Graduate Researcher
University of Maryland, College Park
- Researching generative modeling, representation learning, and 3D perception for point cloud data.
- Contributed to CrossLoc3D and other projects involving scene understanding, robustness, and accelerated simulation.
Mass Spectrometer Calibration Intern
NASA Goddard Space Flight Center
- Developed C++ software for data acquisition and calibration of mass spectrometer systems.
- Worked with DAQs, Raspberry Pi, ADCs, and supporting telemetry-oriented instrumentation workflows.
Education
Formal training in computer science with a strong focus on research and technical depth.
Ph.D. in Computer Science
University of Maryland
M.S. in Computer Science
University of Maryland
B.S. in Computer Science and Mathematics
University of Maryland
A.S. in Computer Science
Anne Arundel Community College
Honors and Affiliations
A few communities and recognitions that have shaped my academic path.
- Mortar Board Senior Honor Society, Adele H. Stamp Chapter
- Phi Theta Kappa Honor Society, Omicron Theta Chapter
- Dean's List across undergraduate semesters since 2018
Contact
The easiest way to reach me is by email. I am also happy to connect through GitHub or LinkedIn.