Teaching Resources, Seminars, and Research Opportunities
"the top part of this webpage seems pretty empty... try to put something nice here, I don't know. give me an image of a confused student who then understands a math concept!" from a conversation with chatty who insists delta x over delta x is the key
Recent and Upcoming Courses
About the Semina material below: Semina is an AI tutor who helps students with problem solving and encourages incremental discovery. Semina was developed by Zilin Jiang. For information about the project and feedback, please contact him.
Fall 2026
Spring 2026
Fall 2025
Spring 2025
Fall 2024
Contact for students
If you are enrolled in a course and need help or accomodations, please contact me at jimmie.adriazola [at] asu [dot] edu.
Seminars
AMPD UP (student-led paper club)
A reading group where participants present and discuss recent papers across applied math, scientific computing, and machine learning. The club emphasizes student leadership, critical reading, and presentation practice.
If you are a student and would like to present, please email me and I'll put you in contact with the student organizers
Data-Oriented Mathematical & Statistical Sciences Seminar (DoMSS)
A seminar series focused on data-driven mathematical and statistical methods, interdisciplinary applications, and invited research talks. I co-organize this seminar with Heyrim Cho; we invite speakers across applied math, statistics, and data science.
Schedule and talks: ASU events — DoMSS. · Co-organizer: Heyrim Cho.
If you would like to present or recommend a speaker, please email me.
Research opportunities
Funded through ASU's Online Undergraduate Research Scholars (OURS) program, this project explores the question:
Can machines, built on mathematics, be taught to discover mathematics itself?
This question has inspired a flurry of exciting research in AI and math reasoning. In this project, we adapt modern statistical learning techniques to show that even simple algorithms, running on ordinary computers, can rediscover classical mathematical formulas such as the area of a circle, the laws of logarithms, and even the prime number theorem. These rediscoveries arise because the methods uncover structure directly from data, not because the rules were preprogrammed or learned from text. Now imagine that same algorithm stumbling upon a brand-new identity, a hidden symmetry, or a surprising relation that no one has ever written down before. That is the thrill this project will chase.
Prerequisites: Differential Equations, Linear Algebra, and some programming experience. Experience with numerical methods preferred but not required.
This project is supported OURS @ ASU. Learn more at OURS @ ASU.
Interested students: please email me with a short note about your background and interest.