Working at the intersection of machine learning, network security and education. Turning ideas into reproducible experiments and practical skills.
Evaluating feature selection and dimensionality reduction across network-security datasets with strict training and evaluation separation.
Investigating temporal confounding and identity overlap in the evaluation of fraud-detection models.
Comparing sequential models and conventional baselines for mobility and handover-related events.
My Python course on Maktabkhooneh: a step-by-step introduction to programming with practical exercises.
A practical learning track covering data analysis, visualization, machine learning and image processing.
Teaching front-end foundations, server-side programming and relational databases.
Teaching networking fundamentals, Cisco concepts and Windows Server services through guided scenarios.
A modular Python workflow for dataset and model selection, configurable hyperparameters, repeated experiments and structured result logging.
A short guide to spotting data leakage and designing more reliable evaluations.