Data Scientist & AI Researcher
Building systems where ideas compete and performance counts
With a background in mathematics and machine learning, I focus on translating theory into tools that work in the real world—especially in finance, where complexity and competition go hand in hand. My current work includes generative models, real-time risk infrastructure, and foundation models for financial modalities based on equivariant architectures.
Machine Learning Research
Deep theoretical experience with temporal graph learning, geometric deep learning, and foundation models for complex applications
Mathematics
Strong theoretical foundation in mathematical principles that underpin modern machine learning and data science
ML Application
Experience developing competitive, winning solutions for specific machine learning tasks across various domains
Big Data
Designing scalable data pipelines and architectures that efficiently process and analyze massive datasets
Algorithm Development & Optimization
Implementing efficient, production-ready algorithms optimized for performance in critical systems
AI System Integration
Bridging the gap between research prototypes and enterprise systems, ensuring seamless deployment of AI solutions
Experience
My professional journey and academic background.
Work Experience
AI Scientist
Summer Intern - Scientist
Student Teaching Assistant
Education
Master of Science in Machine Learning
Bachelor of Science in Mathematics
Atlas
A Foundation Model for Financial Systems
Atlas is a generative foundation model for finance, built to detect fraud, assess risk, and monitor systemic behavior by learning from the structure and flow of transactions.
Using temporal graph neural networks, it captures financial activity as evolving networks—enabling real-time predictions and flexible use across AML, compliance, and forecasting.
Get In Touch
Interested in working together? Feel free to reach out through any of these channels.
sigroll@gmail.com
linkedin.com/in/sigurd-solberg/
GitHub
github.com/sigurdsolberg