Welcome to Tamara Veenstra's Home Page

About Me

I am currently a research mathematician at the Center for Communications Research-La Jolla and Emerita Professor of Mathematics at University of Redlands. I earned my Ph.D. in mathematics from Dartmouth College in 1997. My background area of mathematics is number theory, but I am also interested in machine learning/AI and data science. During my 26 years as a professor of mathematics I enjoyed engaging students with a variety of topics and methodologies and developed a wide variety of teaching materials, some of which are available below.

Classroom Materials (from University of Redlands, 2001-2023)

• Cryptology Textbook: Based on many years teaching an introductory cryptology class, I developed an open educational access textbook for a mathematical cryptology course featuring many interactive and computational explorations.
  

  Cryptology by Discovery An Introduction to Conjecture and Proof, Open Educational Resource Textbook, September 2020.

• Other Classroom Activities
• Additional Cryptology activities that are not in my textbook
• Origami
• Calculus
• Machine Learning Notes
• Historical Codes and Ciphers Notes (Incomplete)

Recent Presentations

• Presentations about my cryptography classes and textbook
  • Crypto-Educators group, January 2023, A tale of two cryptology courses and an open source textbook
  • Crypto-Educators group, April 2025, A discovery based approach for learning RSA
• Presentations about current research interests in computational number theory/machine learning/AI
• West Coast Number Theory, December 2024, Learning Fricke signs from Maass Form Coefficients

Recent Papers

• "Machine Learning the Vanishing Order of Rational L-functions", with Joanna Bieri, Giorgi Butbaia, Edgar Costa, Alyson Deines, Kyu-Hwan Lee, David Lowry-Duda, Tom Oliver, and Yidi Qi, to appear, Mathematics and Machine Learning Proceedings in Advances in Theoretical and Mathematical Physics, arxiv link, 2025
• "Learning Fricke signs from Maass Form Coefficients", with Joanna Bieri, Giorgi Butbaia, Edgar Costa, Alyson Deines, Kyu-Hwan Lee, David Lowry-Duda, Tom Oliver, and Yidi Qi, to appear, Mathematics and Machine Learning Proceedings in Advances in Theoretical and Mathematical Physics, Mathematics and Machine Learning Proceedings Issue, arxiv link, 2025.