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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
If AI had a dating profile, Satyam Mishra would probably be the person it swipes right on. With 5+ years of wrangling code, math, and GPUs (sometimes successfully), he’s been building everything from GPT-powered autonomous agents to robots that can navigate the world without bumping into walls… most of the time.
Specialties? Oh, just the casual stuff: Generative AI, Machine Learning, Computer Vision, Embedded Systems, and Agentic AI. Recently, he’s been flirting with LLM optimization, Koopman-based AI (yes, it’s as fancy as it sounds), optical computing (optical neural networks), and making AI explain why it does what it does without sounding like a politician.
Projects? He’s rolled out grammar-constrained reinforcement learning (because even AI needs to mind its language), fine-tuned LLMs with RAG systems, built contactless facial authentication (so you can look at your computer and it just knows), and cooked up personalized LMS/ERP platforms that make both students and managers slightly less stressed.
He’s also been the brains behind KoopFormer (a physics-inspired transformer that teaches AI motion synthesis, still in progress work) and NeuroExplain (an LLM that talks neuroscience without melting your brain, still in progress work). On the ops side, he’s deployed secure hybrid AI platforms with K3s, Vault, Prometheus, and GitOps: basically making AI run like a well-oiled (and well-guarded) machine.