Oguz Bektas

Data Scientist & Researcher · ML, Deep Learning, LLMs

Industrial AI · Predictive maintenance · Explainable ML

About

Data scientist building machine learning systems for industrial applications. I work across the full stack — from raw sensor signals and time-series feature engineering to deep learning models, LLM-powered tooling, and explainable AI for high-stakes decisions.

My focus is making complex models trustworthy and useful in production: predicting failures before they happen, estimating remaining useful life, and turning black-box predictions into something engineers can actually act on.

Focus Areas

Predictive Maintenance & PHMRemaining useful life estimation, condition monitoring, fault diagnosis, and degradation modeling for industrial assets.

Machine Learning & Deep LearningTime-series models, transfer learning, foundation models, and production ML pipelines.

Large Language ModelsLLM engineering, retrieval-augmented generation, prompt design, and AI-powered workflows.

Explainable AISHAP, feature attribution, model interpretability, and decision-support systems for engineers.

Stack

Python PyTorch TensorFlow scikit-learn PySpark Databricks MLflow Delta Lake SQL Azure LLMs RAG SHAP Time Series Signal Processing

Currently

Working on industrial AI projects — Prognostics, LLM-powered tooling, and applied research with a clear path to production.

Connect

LinkedIn → Google Scholar → ResearchGate → Google Sites → GitHub Pages Academic Profile → X/Twitter → Warwick Grad → Hugging Face → Academia → About.me → GitLab Pages → Medium → Books Google → Vercel → Pages.dev → Netlify → Figshare → Sciprofiles → Carrd • Slideshare •