I am a Data Science and Artificial Intelligence student with a primary long-term interest in Big Data Analytics and Artificial Intelligence research.
I enjoy investigating real-world problems through data, experimentation, mathematical reasoning, and machine learning, while also developing the engineering foundations needed to turn analytical ideas into practical systems.
My experience currently spans areas such as data analytics, machine learning, deep learning, NLP, anomaly detection, optimization, explainable AI, and big data processing.
I am especially interested in problems that combine large-scale data, intelligent computational methods, mathematical thinking, and research-oriented problem solving.
My long-term goal is to work at the intersection of:
Big Data · Artificial Intelligence · Research
- Big Data Analytics
- Artificial Intelligence Research
- Machine Learning
- Data Engineering
- Deep Learning
- Applied Mathematics for AI and Data Science
- Natural Language Processing
- Anomaly Detection
- Unsupervised Learning
- Explainable AI
- Optimization
- Mathematical Modeling and Simulation
- Reinforcement Learning
- Data Visualization and Business Intelligence
I am particularly interested in work that requires a combination of data analysis, experimentation, mathematical modeling, and computational methods.
Research-oriented machine-learning project investigating flow-level, entropy-based, and statistical representations for anomaly detection in industrial control system network traffic.
The project compares supervised and anomaly-detection approaches across multiple feature representations and evaluates their behavior using metrics such as accuracy, F1 score, false-positive rate, and false-negative rate.
Focus: Machine Learning · Anomaly Detection · Cybersecurity · Feature Engineering · Experimental Analysis
Big data analytics project using PySpark for large-scale data processing and analysis, with analytical findings communicated through Power BI.
The project combines data preparation, exploratory analytics, predictive analysis, and business-oriented visualization.
Focus: PySpark · Big Data · Data Analytics · Power BI · Data Visualization
End-to-end Arabic NLP project based on the AAFAQ dataset covering question classification, generative question answering, and Arabic-to-English translation.
The project compares traditional machine-learning methods, BERT-based classifiers, several generative QA architectures, and integrates the selected components into a Streamlit application.
Focus: NLP · Transformers · AraBERT · AraGPT2 · Machine Learning · Streamlit
AI-based EV charging intelligence system combining predictive analytics with several intelligent decision-support techniques.
The project includes regression, classification, anomaly detection, SHAP and LIME explainability, fuzzy logic, clustering, and reinforcement learning within an integrated application.
Focus: Applied AI · Explainable AI · Reinforcement Learning · Fuzzy Logic · Machine Learning
Experimental project exploring both classical and metaheuristic optimization techniques.
The work includes constrained and unconstrained optimization, wrapper-based feature selection, and comparative experiments with Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimization.
Focus: Optimization · PSO · GA · GWO · Feature Selection · Experimental Evaluation
Python · SQL · Java · Pandas · NumPy
Scikit-learn · PyTorch · TensorFlow/Keras · Transformers · XGBoost
PySpark · Power BI · Data Analysis · Data Visualization
NLTK · PyArabic · Hugging Face Transformers · BERT
SHAP · LIME · Fuzzy Logic · Reinforcement Learning · Anomaly Detection
Jupyter Notebook · Streamlit · Flask · MySQL · GitHub
Completed the coursework of the Mathematics for Machine Learning and Data Science Specialization, covering:
- Linear Algebra for Machine Learning and Data Science
- Calculus for Machine Learning and Data Science
- Probability & Statistics for Machine Learning and Data Science
I also consolidated the material into my own handwritten study notes as part of my learning process.
Currently progressing through the Deep Learning Specialization, with 3 of its 5 courses completed:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
Completed coursework covering topics including:
- Supervised Learning
- Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning
- Recommender Systems
- Reinforcement Learning
I am continuing structured study in C++ programming and problem solving to strengthen my programming, algorithmic thinking, and broader computer science foundations.
I am actively developing my foundations in:
- Git and collaborative version-control workflows
- C++ programming and problem solving
- Scalable data processing and Data Engineering
- Deep Learning
- Unsupervised Learning and Clustering
- Mathematical Modeling and Simulation
- Numerical methods such as Euler's Method
- Reinforcement Learning
- Web development using HTML, CSS, JavaScript, and PHP
- Research methodology and experimental evaluation
My goal is not only to learn individual technologies, but to understand how mathematical, analytical, computational, and engineering methods can work together to solve complex problems.
Beyond my technical work, I have strong personal interests in mathematics, psychology, and philosophy.
Mathematics is particularly important to me, both for its applications in machine learning and data science and as a subject I intend to continue studying more deeply.
I am also interested in questions related to:
- Mathematical and logical reasoning
- Human behavior
- Decision-making
- Knowledge and how conclusions are formed
- The interaction between humans, data, and intelligent systems
I also enjoy writing, research, and public speaking, which have helped me become more comfortable communicating ideas to different audiences.
These are broader intellectual interests rather than areas in which I claim formal professional specialization.
Additional academic and foundational work is available in:
My primary long-term direction is toward Big Data and Artificial Intelligence research.
At this stage of my career, I am also interested in gaining practical experience across areas such as:
- Big Data Analytics
- Data Analytics
- Data Engineering
- Machine Learning Engineering
- AI Engineering
- Deep Learning
I see these areas as complementary foundations rather than completely separate career paths.
Working with real-world data, building analytical pipelines, understanding scalable data systems, developing machine-learning solutions, and evaluating models rigorously can all contribute to the research and data-intensive AI work I hope to pursue in the future.