PhD Candidate · Geospatial AI · Remote Sensing · Scientific ML
I build machine learning pipelines that turn UAV, satellite, and Earth Observation data into measurable environmental insight.
I work at the intersection of remote sensing, deep learning, and scientific data engineering.
My focus is building reliable AI systems for environmental monitoring: from UAV and VHR imagery to large-scale geospatial inference, segmentation, canopy analysis, and explainable models.
- Geospatial AI: UAV imagery, orthophotos, satellite data, raster/vector pipelines
- Remote sensing ML: segmentation, canopy mapping, land-cover analysis, large raster inference
- Foundation models: DINO-style backbones, ViTs, feature extraction, weak supervision
- Explainable AI: attribution, attention diagnostics, domain shift, spurious cue analysis
- HPC: SLURM, Docker, GPU pipelines, reproducible experiments
Python |
Julia |
Rust |
C++ |
Bash |
PyTorch |
TensorFlow |
Keras |
scikit-learn |
NumPy |
Pandas |
OpenCV |
Linux |
Docker |
Kubernetes |
Git |
AWS / S3 |
YAML |