Email the Author
You can use this page to email Satyam Mishra about AI Meets Pure Math: Issue #1.
About the Book
AI Meets Pure Math: Issue #1
Koopman Operators in NLP
Modeling Semantic Drift & Dynamics with Pure Mathematics
Built for the curious. Loved by the rigorous.
What if you could watch the meaning of words evolve over time, not just guess what they mean today, but forecast where they’re heading tomorrow?
This book introduces a groundbreaking approach at the intersection of Natural Language Processing (NLP) and dynamical systems theory: Koopman Operator Theory. Originally designed to study fluid flows and chaos, this mathematical framework now takes on a new frontier: semantic drift in language.
📘 What’s Inside:
- A gentle yet rigorous introduction to discrete-time dynamics and Koopman lifting
- How to track and forecast semantic evolution using real word/sentence embeddings
- Full Python implementations, including matrix estimation, spectral analysis, and visualizations
- Applications of Koopman models to sentence drift, discourse modeling, forecasting, and interpretability
- Advanced architectures: Koopman autoencoders, latent space dynamics, and future NLP integrations
Whether you're an NLP researcher, applied mathematician, or a curious learner fascinated by how ideas transform, this book will change how you view language, not as static data, but as motion through meaning space.
🔍 Key Topics:
- Semantic Drift Modeling
- Koopman Regression from Embeddings
- PCA/t-SNE Visualization of Meaning Flow
- Spectral Decomposition & Forecasting
- Koopman Autoencoders & Latent Operators
- Real-World Applications + Limitations
✍️ Author Bio:
Satyam Mishra is an AI researcher and mathematical systems designer with over 20 peer-reviewed publications in Q1 SCIE journals and A* conferences. He’s built Koopman-powered NLP tools, published patents on spectral learning and sparse Transformers, and led global research teams from Switzerland to Vietnam. This book is part of his series to revive mathematical elegance in AI.
🎓 Ideal for:
- AI Researchers
- Applied Mathematicians
- NLP Engineers
- Graduate Students
- Curious minds looking for elegant thinking tools
🌐 Follow the Series:
- Medium: satyamcser.medium.com
- LinkedIn: linkedin.com/in/satyamcser
About the Editor
Satyam Mishra is an AI researcher, applied mathematician, and systems builder specializing in neural architectures, semantic dynamics, and operator theory. With over 20 peer-reviewed research publications in Q1 SCIE journals and A* conferences, his work bridges natural language processing, Koopman dynamics, spectral learning, scientific machine learning, and explainable AI.
He has led multiple international AI projects across Switzerland, Vietnam, and India, developed production-grade GenAI systems, holds multiple patents on Koopman-based NLP, 3D Sparse Transformers, and spectral learning, and built AI-powered educational platforms. His research includes contributions to operator-based attention, symbolic drift modeling, and real-time multimodal inference.
Satyam currently serves as a Senior Tech Lead at Vision Mentors, with prior affiliations to SISLAB, VNU Hanoi and as an Independent AI Consultant/Researcher at Verysell AI (Switzerland). He earned his Bachelor’s degree (High Distinction/Valedictorian) from Vietnam National University, Hanoi.