🔹 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!

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

• 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

1. 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

2. 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

3. Neural Network Exploration
   🔹 Deep learning implementations from scratch
   🔹 Built neural networks including CNNs using PyTorch
   🔹 Conducted performance experiments on various architectures

4. Bayesian Machine Learning
   🔹 Explored uncertainty in deep learning
   🔹 Applied Bayesian techniques in neural network training using PyTorch

5. 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

6. 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

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

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