ME x ML Engineer — Mechanical engineer building production ML systems for real engineering and financial problems.
Most profiles are CS grads doing sentiment analysis on IMDB. I build ML that solves actual engineering problems — because I've spent years doing the engineering first.
Physics-Informed Deep Learning — Neural surrogates that predict structural analysis 1000x faster than FEA. Deep ensembles with calibrated uncertainty. Loss functions that encode beam theory, not just MSE.
Domain-Adapted LLMs — Fine-tuned Qwen2.5-3B on 5K+ mechanical engineering Q&A pairs. Custom MechEval benchmark proves the 3B model outperforms the base 7B on domain tasks.
Agentic AI Systems — Custom ReAct agent with 7 tools for market microstructure analysis. Hand-built loop (no LangChain), two-tier cache, provider failover, rate limiting.
| Project | What It Does | Stack | Demo |
|---|---|---|---|
| fea-surrogate | Physics-informed neural surrogate for structural analysis | PyTorch, Gradio, Docker, CI/CD | Live |
| mechspec-qwen | Domain-adapted LLM for engineering specs and calculations | Transformers, PEFT/LoRA, Gradio | Live |
| agentic-market-analyzer | Custom ReAct agent for market analysis — no frameworks | Qwen3-8B, yfinance, FRED, Gradio | Live |
ML/AI: PyTorch, Transformers, PEFT/QLoRA, Gradio, Deep Ensembles, Physics-Informed Loss Engineering: Euler-Bernoulli, Kirchhoff-Love, Lame equations, FEA/FEM, GD&T Infrastructure: Docker (multi-stage), GitHub Actions, MLflow, model versioning Full-Stack: React, Node.js, WebSockets, D3.js, SQLite, Svelte
- Mechanical engineer (ME) — structural analysis, FEA/FEM, GD&T
- ME + ML crossover: I understand both the physics and the models
- Active trader — built Monte Carlo options analyzer, real-time market terminal
- NYU engineering
- 203 tests across 3 ML projects, all passing