Data Professional specializing in building scalable data infrastructure, cloud ETL pipelines, and intelligent AI/ML systems. I bridge the gap between advanced data science, spatial analytics, and machine learning workflows.
- Data Engineering: Architecting automated cloud ETL pipelines (AWS S3, Redshift), REST API ingestion frameworks, and end-to-end data integrity systems.
- Computer Vision & Deep Learning: Engineering self-supervised preprocessing pipelines, generative modeling, and containerized (Docker) CNNs for industrial defect detection.
- Geospatial & Time Series: Processing multi-modal datasets, NetCDF satellite data (GRACE/GLDAS), and building spatiotemporal models.
- 🤖 Deep Learning & GenAI: PyTorch, Self-Supervised Learning, RAG Systems, Generative Modeling.
- 👁️ Computer Vision: Image enhancement, denoising, object detection, and satellite image segmentation.
- 📈 Time Series & Signal: Hydrological trend analysis, sensor telemetry processing, and audio/signal deep learning.
- Scaling geometric and spatial deep learning architectures for large-scale multi-modal datasets.
- Optimizing preprocessing latency for high-variance sensor and real-time telemetry pipelines.
- Exploring advanced downstream evaluation metrics (FID, Inception Score) in generative modeling and LLM/RAG workflows.
💼 Let's Connect: [Portfolio Website Link]
