Springer Math

06/06/2026

Springer author Javad Mashreghi with his book at the Canadian Math Society Summer conference 📚 🇨🇦

06/03/2026

📢 Springer Mathematics at the CMS Summer Meeting 2026!
We’re excited to share that Springer will be attending the Canadian Mathematical Society (CMS) Summer Meeting in New Brunswick, Canada this summer!

🔗 Learn more about the conference:
https://summer26.cms.math.ca/

📚 Are you interested in publishing a book?
💡 Curious about Springer’s mathematics program?
🤝 Or simply want to connect and chat?

Come visit us at the Springer booth and meet Springer Editor Donna Chernyk, who will be happy to:

discuss your book ideas and proposals
provide guidance on publishing with Springer
answer any questions about Springer Mathematics titles and series

We look forward to meeting mathematicians, researchers, and authors from across the community—see you in New Brunswick!

The Canadian Mathematical Society (CMS) extends an invitation to the mathematical community for the 2026 CMS Summer Meeting scheduled from June 5-8, 2026, in Saint John, New  Brunswick. The four-day event will feature prize lectures, plenary speakers, scientific sessions, along with mini-courses ...

06/03/2026

New Book Release from Springer Mathematics!
We’re excited to announce the publication of the latest edition of
A Course of Stochastic Analysis by Alexander Melnikov—now available!
🔗 Get your copy: https://link.springer.com/book/10.1007/978-3-032-20482-0

📖 About the Book
This thoroughly updated second edition provides a clear and modern pathway from the foundations of probability theory all the way to stochastic calculus and its applications in finance, statistics, and risk analysis.
From martingales and semimartingales to stochastic differential equations (SDEs), this book offers a unified perspective across discrete and continuous time frameworks—making complex ideas accessible and practical.

✨ What’s New in the 2nd Edition
✅ First textbook treatment of optional stochastic analysis on non-standard filtrations
✅ New theory for optional SDEs and stochastic exponentials/logarithms
✅ Expanded applications in:

📊 Stochastic regression analysis
⚠️ Risk theory and ruin probability
✅ Extensive new exercises with solutions (see Supplement Chapter 15)

👩‍🎓 Who Should Read It?
Perfect for:

Senior undergraduate and graduate students
Instructors teaching probability and stochastic processes
Researchers and practitioners in finance, statistics, and risk modeling

With abundant worked examples and a strong balance between theory and application, this book is ideal for self-study or classroom use.

📚 Part of the CMS/CAIMS Books in Mathematics series (Volume 17)

This book offers a unified, modern pathway from the Kolmogorov foundations of probability to the tools of stochastic calculus

06/02/2026

Collaboration or solo research?

05/27/2026

📘 New Book Release

We’re excited to announce the publication of:
Machine Learning in Data Processing by Xiang-Sheng Wang and Chisheng Wang

Part of the Forum for Interdisciplinary Mathematics (FFIM) series, this new title offers a clear, rigorous introduction to the mathematical foundations of machine learning—perfect for students and researchers who want to understand the theory without focusing on coding.

✨ What makes this book stand out?

Detailed mathematical treatment of:

Linear and nonlinear regression
Regularization techniques
Fundamentals of neural networks

Designed for a one-semester undergraduate course
Ideal for math learners who want conceptual clarity without programming

📚 Who should read it?

Mathematics students and researchers exploring machine learning
Scientists and engineers seeking a deeper theoretical understanding
Anyone interested in the why behind machine learning methods

This book bridges the gap between traditional mathematics and modern data science, emphasizing intuition, derivation, and mathematical insight.

🔗 Learn more and get your copy:
https://link.springer.com/book/10.1007/978-3-032-20855-2

Upper undergraduate textbook, explaining in detail the mathematical ideas and derivations of linear regression, regularization, nonlinear regression.

05/27/2026

Every math student should read ______.

05/22/2026

BREAKING NEWS: Toshiyuki Kobayashi , has been awarded the 67th Fujihara Award 🏆 for his outstanding contributions to "Mathematical Analysis of Infinite-Dimensional Symmetries". 👉 https://ms.u-tokyo.ac.jp/news_e/12034_e.html

For a comprehensive overview of Professor KOBAYASHI's work, see:
Open Access Chapter 🎉
Goldman, W.M. et al. (2025). The Mathematical Work of Toshiyuki Kobayashi
https://link.springer.com/chapter/10.1007/978-981-97-8449-3_1

05/20/2026

We are delighted to announce the publication of Ergodic Theory by Alex Blumenthal and Lai-Sang Young—a clear, rigorous, and engaging introduction to one of the most powerful frameworks for understanding dynamical systems.
Ergodic theory transforms seemingly chaotic or random behavior into structures that can be analyzed through probability, revealing deep connections across mathematics and beyond. This new volume offers a concise yet comprehensive treatment, making it ideal both as a graduate textbook and a reference for researchers in pure and applied mathematics.
✨ What’s inside?

Part I (Ch. 1–7): Foundations of ergodic theory, including invariant measures, ergodicity, mixing, entropy, and the Shannon–McMillan–Breiman Theorem
Part II (Ch. 8–13): Continuous maps on metric spaces and the full range of invariant measures
Part III (Ch. 14–16): Advanced topics rarely covered at this level, including SRB measures, their links to entropy and Lyapunov exponents, and extensions to random and infinite-dimensional systems

Throughout, the authors highlight both the mathematical elegance and the practical relevance of ergodic theory, with connections to areas such as information theory, stochastic processes, and beyond.

🔗 Learn more and get your copy:
https://link.springer.com/book/10.1007/978-3-032-08836-9

This book describes ergodic theory, an approach to dynamical systems which casts disordered and seemingly random behavior in frame of probability theory.

05/20/2026

What’s a topic you wish had a clearer modern reference?

05/14/2026

What’s your go-to “math joke”?