Applied ML engineer working on predictive maintenance and reliability — Bayesian state estimation and survival analysis, plus the systems that run them in production.
Founded Kaful, building prognostic digital twins for industrial equipment. Open to hands-on engineering roles in industrial AI and manufacturing.
kaful-data-twin — Streaming CNC digital twin, deployed as multi-tenant SaaS. Estimates tool wear cut-by-cut from raw cutting-force data with a particle filter and forecasts remaining useful life. Session auth, per-tenant isolation, concurrent ingest (~2× throughput, with the remaining ceilings documented). Postgres + R2 on Render. Live demo →
Main finding: cross-tool RUL transfer fails because of the observation model — the wear→force map — not the degradation rate. Isolated with a 2×2 study, reproducible from the repo.
Kaful agentic pipeline — Compiles OEM manuals into executable physics-based digital twins (RAG + LLM extraction → simulation → Monte Carlo RUL) with no historical failure data. Validated across four machines. Presented at IWSM 2026.
Battery_RUL_CNN — PyTorch CNN predicting lithium-ion battery RUL from single-cycle discharge data. R² ≈ 0.76 on held-out cells.
SurvivalSimulation — Out-of-sample comparison of survival models (Cox PH, DeepSurv, RSF, GBSA) under varying censoring.
Python · PyTorch · PostgreSQL · TypeScript · Next.js
Particle filtering · survival analysis · agentic LLM/RAG pipelines · streaming ingest & concurrency · multi-tenant services