DavidOriel

DavidOriel.github.io

🚀 Hi, I’m David

🔹 Teaching Assistant in Algorithms, Intro to Machine Learning, Data Mining, and Calculus 1
🔹 Passionate about Machine Learning, NLP, and AI Research
🔹 Always open to exciting projects and collaborations!

📌 Areas of Expertise

🔹 Theoretical Knowledge

Machine Learning Theory: Bayesian ML, Reinforcement Learning, Graph Machine Learning
Mathematics: Applied Mathematics, Differential Geometry
Statistics: Probability, Big Data Analytics

🔹 Practical Skills

Applied ML: Computer Vision, Neural Networks, Transformers
Data Science & Engineering: Feature Engineering, Large-Scale Data Processing
AI Deployment: Cloud-Based ML Models, MLOps, Model Optimization

📂 Featured Projects & Repositories

Political Offensiveness Against the Judicial Authority
🔹 NLP project analyzing political rhetoric
🔹 Fine-tuned Hebrew-BERT model for sentiment and stance classification
🔹 Conducted statistical analysis of linguistic patterns
Probabilistic Graphical Models
🔹 Projects in probabilistic modeling and reinforcement learning
🔹 Implemented HMM, EM algorithm, and Gibbs sampling
🔹 Developed a Q-learning agent for game-solving
Neural Network Exploration
🔹 Deep learning implementations from scratch
🔹 Built neural networks including CNNs using PyTorch
🔹 Conducted performance experiments on various architectures
Bayesian Machine Learning
🔹 Explored uncertainty in deep learning
🔹 Applied Bayesian techniques in neural network training using PyTorch
Urban Cluster Statistics
🔹 Urban planning meets data science
🔹 Web app powered by Momepy for statistical analysis over GIS data (GDB/SHP files)
🔹 Built for social good and data-driven planning
Machine Learning Basics
🔹 Coursework and practical exercises from an Intro to Machine Learning course
🔹 Covered foundational ML techniques: decision trees, SVMs, ensemble methods, and more

📬 Contact Me

📧 Email: davoriel@gmail.com
🔗 LinkedIn: linkedin.com/in/davidoriel

Thanks for visiting! . 🌟