I'm a passionate researcher and developer specializing in Probabilistic Machine Learning and Artificial Intelligence. My work focuses on creating clean, efficient, and well-documented libraries for probabilistic reasoning and uncertainty modeling.

I develop tools and frameworks that make probabilistic machine learning more accessible and practical:

• 📊 Probabilistic Models: Building unified interfaces for various probabilistic models
• 🎲 Random Events: Creating clean abstractions for probabilistic reasoning
• 🧠 Tractable Circuits: Working on probabilistic abstract tractable circuits
• 🤖 Cognitive Architectures: Developing intelligent systems with pycram and semantic_world

Probabilistic Models in Python - A unified interface for probabilistic models, similar to what sklearn provides for classical ML.
📚 Documentation

Clean Random Events for Probabilistic Reasoning - Common interface for describing random variables and events shared across ML packages.
📚 Documentation

• Random Events Lib - C++ implementation for performance-critical applications

Languages: Python, C++, SQL Focus Areas: Probabilistic Machine Learning, Uncertainty Quantification, Software Engineering
Principles: Clean Code, Comprehensive Documentation, Test-Driven Development

I'm actively developing tools that bridge the gap between theoretical probabilistic models and practical implementations, with emphasis on:

• Creating intuitive APIs for complex probabilistic operations
• Ensuring high performance through optimized C++ implementations
• Maintaining comprehensive documentation and testing
• Building reusable components for the probabilistic ML community

Let's build the future of probabilistic machine learning together! 🚀