I build production ML systems and I’m especially interested in the intersection of: LLMs / multimodal ↔ hardware + signals ↔ real-world decision-making.

• 🔭 Current focus: ML for diagnostics / reliability / repair recommendations, plus modern NLP on messy log data (ranking, calibration, uncertainty, cost-sensitive decisions).
• 🌱 Learning: multimodal foundation models, LLM finetuning, and practical evaluation/guardrails.
• 🛰️ Background: PhD EE (electromagnetics / metamaterials). Deep experience with RF modeling, phased arrays, and EM simulation.
• 👯 Open to collaborating on: applied ML projects involving RFID, wireless, phased arrays, signal + text fusion, or high-signal evaluation.
• 💬 Ask me about: PyTorch, transformers for ranking/classification, ML for hardware systems, EM/RF simulation, phased-array modeling.

Python • PyTorch • Hugging Face • scikit-learn • Polars • Jupyter
AWS (SageMaker / Athena-ish workflows) • Docker • Git • Linux
MATLAB • HFSS / CST / FEKO / ADS (and friends)

• Phased-Array-Antenna-Model Phased-array antenna pattern modeling and analysis — practical, engineering-first code and examples.
• phased-array-systems Systems-level phased-array concepts and implementation notes/tools for real-world arrays.
• ☕ BeanBench (iOS) An iPhone app for specialty coffee nerds: log brews, rate beans, and build better cups over time.
• PyTorch-Vision-Transformers-ViT Vision Transformer work in PyTorch, experiments, implementation details, and learning artifacts.

If you’re working on any of these, I’d love to chat:

• Multimodal (text + sensors/signals) modeling
• RFID / wireless inference problems
• Phased arrays + ML (surrogates, optimization, generative design)

• LinkedIn: https://linkedin.com/in/jhodge007
• GitHub: https://github.com/jman4162
• Email: jah70 at vt dot edu

🏀 Duke basketball • 🥾 hiking • ☕ specialty coffee (V60 life)