Research Focus
AI, machine learning, intrusion detection systems, feature engineering, data science, and network security.
Open to PhD opportunities in Europe
Building reliable AI pipelines for network security and IoT intrusion detection.
Machine Learning Researcher · Data Analyst · Python Instructor · IT Administrator
I combine academic research in machine learning and computer networks with real-world experience in IT administration and programming education. My current research focuses on optimized feature engineering, leakage-free machine learning pipelines, and robust intrusion detection for IoT and network security environments.
Mohammadali Mousavireineh
Machine Learning Researcher · Data Analyst · Python Instructor · IT Administrator
M.Sc.
Computer Networks
19.43/20
Graduate GPA
2016+
IT Administration
2018+
Teaching & Training
Profile
Researcher with applied engineering depth
My background connects three practical worlds: machine learning research, operational IT infrastructure, and programming education. This combination helps me design research that is not only methodologically controlled, but also meaningful for real network and cybersecurity systems.
Applied IT Background
Server and client maintenance, internal IT support, troubleshooting, security monitoring, and organizational data support.
Teaching Profile
Python, data analysis, computer vision fundamentals, frontend and backend web development, and practical programming projects.
Research
Featured Research
Manuscript under review · Multimedia Tools and Applications (Springer)
Optimized Feature Engineering for IoT Intrusion Detection: Synergy of Feature Selection, Feature Extraction, and Classifier Ensemble
This study investigates how carefully designed feature selection and feature extraction pipelines can improve machine learning-based intrusion detection in IoT and heterogeneous network environments. A unified, leakage-free experimental framework is evaluated across UNSW-NB15, AWID, and CSE-CIC-IDS2018. The work compares filter-based feature selection methods, feature extraction techniques, twelve classifier families, and stacking-based meta-learning to identify robust and efficient IDS configurations.
Leakage-free, cross-dataset IDS benchmark
Comparison of FS-only and FS→FE pipelines
Ablation-driven analysis of each pipeline component
Focus on efficient and scalable IDS design for IoT environments
Pipeline
Pipeline-oriented methodology
Preprocessing
Cleaning, encoding, stratified partitioning, and training-only fitting to avoid information leakage.
Feature Selection
Variance Threshold, ANOVA F-test, and Chi-Squared filters to reduce redundant attributes.
Feature Extraction
PCA, LDA, ICA, and truncated SVD to produce compact discriminative representations.
Modeling & Ensemble
Classical, ensemble, boosting, and stacking classifiers evaluated under a controlled protocol.
Career
Experience
I am seeking PhD opportunities in Europe where I can continue research at the intersection of AI, cybersecurity, data science, and computer networks. I am particularly interested in reliable ML pipelines, IDS robustness, feature engineering, explainable security analytics, and AI-assisted network protection.
Researcher – IoT Intrusion Detection Project
Shomal University / Research Collaboration
Designed and evaluated feature engineering pipelines for intrusion detection using Python, Scikit-learn, benchmark datasets, and ensemble learning strategies.
IT Administrator
Social Security Organization, Iran
Maintaining servers, client systems, internal IT infrastructure, security monitoring, troubleshooting, technical support, and data management.
Programming and Data Analysis Instructor
Pishrorayaneh Institute & Rayanamol Institute, Amol
Teaching Python, data analysis, computer vision fundamentals, frontend/backend web development, and project-based programming skills.
Stack
Technical Skills
Programming
Python
JavaScript
PHP
SQL / MySQL
HTML5
CSS3
Machine Learning & AI
Scikit-learn
TensorFlow
PyTorch
Feature Engineering
Ensemble Learning
Model Evaluation
Data Analysis
Pandas
NumPy
Data Cleaning
Dimensionality Reduction
Experimental Design
Domains
Network Security
Intrusion Detection Systems
Computer Vision
IT Administration
Cybersecurity
Academic
Education
PhD Research Direction
I am seeking PhD opportunities in Europe where I can continue research at the intersection of AI, cybersecurity, data science, and computer networks. I am particularly interested in reliable ML pipelines, IDS robustness, feature engineering, explainable security analytics, and AI-assisted network protection.
M.Sc. in Computer Engineering – Computer Networks
Shomal University, Amol, Iran
GPA: 19.43/20 · Top student · Thesis: Optimized Feature Engineering for IoT Intrusion Detection
B.Sc. in Software Engineering
Shomal University, Amol, Iran
GPA: 17.50/20
Certifications & Languages
Certificates in Data Analytics, Machine Learning, Machine Vision, UI/UX Design, and Cybersecurity. Languages: Persian Native, English B2, German A2.
Contact
Let’s connect
For PhD supervision opportunities, academic collaboration, research discussion, or professional projects, please send a message.