Director of Data Science | Applied Mathematics | Machine Learning & Cognition
I build and lead machine learning systems grounded in mathematics, informed by cognitive science, and designed for real-world use.
My background spans applied mathematics, statistics, psychology, and machine learning. Over the past decade, I have worked on forecasting systems, recommendation engines, NLP pipelines, and AI platforms supporting large-scale products. I am especially interested in how learning systems represent information, generalize, and adapt.
Alongside industry work, I focus on teaching and research-driven exploration through writing, code, and structured learning artifacts.
CognitiveMLStudio is my long-term educational and research lab exploring the foundations of machine learning, mathematics, and cognition.
The studio is organized around:
- Mathematical intuition for learning systems
- Probability, optimization, and learning dynamics
- Cognitive and symbolic perspectives on AI
- Graphs, networks, and representation
🔗 https://github.com/swidvey/CognitiveMLStudio
- Mathematical representations of learning and generalization
- Hybrid symbolic–statistical systems
- Cognitive models of abstraction and curiosity
- Interpretability through structure and geometry
- Graphs, diffusion, and networked systems
I care about AI systems that are mathematically grounded, cognitively informed, and interpretable by design. I am less interested in novelty for its own sake and more interested in understanding why learning systems work.
