Machine Learning Engineer with production experience across detection, segmentation, 3D perception, and geospatial applications. Architected and deployed real-time CV pipelines utilizing YOLO, RF-DETR, SAM, and advanced tracking frameworks (ByteTrack, DeepSORT). Engineered multi-sensor satellite imagery (SAR/EO/NIR) analytics, Vision-Language Model (VLM) fine-tuning, and SAHI/OBB remote sensing pipelines using GDAL, Rasterio, and QGIS. Expert in 3D coordinate transformations (URDF), point cloud processing, and concurrent robotic navigation platforms. Specialized in model optimization (ONNX, quantization) and scalable deployment (FastAPI + Docker); active OSS contributor.
Autonomous satellite intelligence system. Fine-tuned LiquidAI LFM2.5-VL-450M on VRSBench (29K satellite images, 123K VQA pairs) using LoRA via TRL+PEFT. Built multi-spectral analysis pipeline computing NDVI, NBR, NDWI, NDRE with temporal change detection for deforestation monitoring.
Trained YOLOv8s-OBB on ROBOX-SSDD (1,160 images, 2,587 ships) achieving 98% mAP@50. Evaluated cross-domain generalization on custom Umbra X-band GEC chips. Built SAHI inference pipeline with geo-referenced OBB output (lat/lon + heading) using rasterio affine transforms.
Tracking and analytics pipelines across volleyball, football, and basketball. Hybrid CPU/GPU architecture using ByteTrack, RT-DETR, and custom ONNX models achieving real-time inference (30-100 FPS) with zero-shot team classification via SigLIP embeddings.