Ph.D. in Electrical & Computer Engineering (University of Delaware)
Machine learning, generative models, satellite imaging, and applied computer vision.
Actively seeking Machine Learning / Applied Scientist / Research Scientist roles.
📍 Newark, DE, USA
📫 af.ramirez236@gmail.com
🔗 Google Scholar
🔗 LinkedIn
🌐 https://anfera.github.io/
I work at the intersection of machine learning and remote sensing, developing generative and transformer-based pipelines for LiDAR and hyperspectral data. I’ve contributed to industry-scale ML deployments at Apple and Vertex Pharmaceuticals, and I lead research collaborations with NASA and academic partners in Poland and Ukraine. I enjoy bridging theory and practical implementation, with an emphasis on reproducible research and real-world impact.
Python · PyTorch · TensorFlow · CUDA · Transformers · RAG · Docker · AWS/GCP · Git
- HHDC – Hyperheight Data Cube Denoising & Super-Resolution
Public dataset of ~89k 3D photon-count LiDAR cubes built from NEON discrete-return LiDAR for denoising and spatial super-resolution of forest canopies.
📦 Hugging Face: anfera236/HHDC • License: CC BY 4.0
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Apple – LLM & Generative AI Intern (2024)
Developed retrieval-augmented generation (RAG) pipelines, fine-tuned Apple Intelligence models, and designed evaluation/stress-testing frameworks for production LLMs. -
Vertex Pharmaceuticals – ML Engineer (2023)
Built medical diagnostic segmentation models for kidney and pulmonary fibrosis imaging achieving >90% accuracy. -
University of Delaware – Research & Teaching Assistant (2022–present)
Conduct ML research for LiDAR & hyperspectral data; published in IEEE venues; part of NASA CASALS.
University of Delaware — Ph.D. Electrical and Computer Engineering
University of Los Andes — M.Sc. Computer & Electronic Engineering
University of Los Andes — B.Sc. Electronic Engineering
Thanks for visiting my profile — feel free to reach out for research collaborations, ML consulting, or interesting datasets to explore.