IBM Granite Soccer Analyst
PythonExplainable soccer tactical analysis — StatsBomb tracking data, bounded Voronoi zone-control diagrams, and IBM Granite LLM insights in an interactive Streamlit app.
View repository →AI Researcher & Applied Mathematician
BSc Computer Science @ Baku State University · Dual Degree @ Holon Institute of Technology.
Focused on machine learning, computer vision, graph theory, and the mathematical foundations of intelligence.
I'm a Computer Science student at Baku State University (SABAH group) pursuing a dual degree at Holon Institute of Technology, Israel. My academic journey sits at the intersection of pure mathematics and applied artificial intelligence.
My research interests span machine learning theory, computer vision, graph algorithms, and the fascinating geometry of origami tessellations — where abstract mathematical structures reveal surprising computational properties.
I've competed at ICPC Azerbaijan Regionals, placed at Robotex Eurasia & Turkey Championships, and earned recognition from the Rector of Baku State University. I believe rigorous mathematical thinking is the foundation of trustworthy AI.
I've participated in multiple hackathons and innovation competitions, including the Viveka Creation Program by Pasha Holding and the Innovation Agency, and pitched projects at FICS 2024. I'm passionate about translating research into practical, impactful solutions.
Previously worked as a Technician at BSU TETYM, where I conducted data analysis and built dashboards to support research-driven decision making.
Explainable soccer tactical analysis — StatsBomb tracking data, bounded Voronoi zone-control diagrams, and IBM Granite LLM insights in an interactive Streamlit app.
View repository →A deep-learning system using CNNs (MobileNetV2) to classify intricate paper models, with a Groq LLM giving real-time, step-by-step folding instructions from visual input.
View repository →A feed-forward neural network for handwritten-digit classification on the MNIST dataset, built and trained from the ground up.
View repository →Core machine-learning algorithms — K-Means, Gaussian Mixture Models and SVM — implemented from first principles, alongside dimensionality-reduction notebooks.
View repository →Hundreds of algorithmic solutions across Codeforces, CSES and Eolymp — data structures, dynamic programming, graphs and more.
View solutions →The role of eigenvalues and eigenvectors in optimizing AI algorithms and understanding the structure of data.
Read article →The mathematical derivation of PCA with a step-by-step NumPy implementation for dimensionality reduction.
Read article →A verified researcher profile linking my publications, contributions and academic work.
View ORCID →Open to research collaborations, data science projects, and interesting mathematical problems. Feel free to reach out!