The CASCADE 2025 will offer two minitutorials to the participants, with (tentative) schedule of evening of Friday April 25 and morning of Sunday April 27. The goal of the tutorials is to offer basic training and regional network opportunities and to supplement the main activities of the RAIN conference on Saturday, April 26, with additional learning and networking opportunities for students and early-career researchers.
During the tutorials, instructors will provide brief demonstrations. Next, the mentors from Oregon State University paired individually to the participants will be available to provide hands-on help. Sessions are expected to accommodate 5-10 trainees, ensuring an intimate and focused learning environment.
The participants will
Develop New Skills: these tutorials offer a chance to build foundational skills or dive into intermediate concepts in computational modeling and data science.
Make the Most of Your Time: Arriving early or staying after the conference? These Minitutorials provide a valuable activity for participants already on campus before or after the main event.
The description of the tutorials is provided below.
Friday, April 25 18:00-21:00
Location: Oregon State University, Kidder Hall
18:00-18:30 arrival and dinner
18:30-21:00 Minitutorial I
Title: Introduction to Computational Modeling
Goals: This tutorial will give participants hands on experience with one or more of the following:
(1) (BASIC) Introduction to programming, plotting, and using libraries
(2) (INTERMEDIATE) Transition to the programming in compilable environments
(3) (INTERMEDIATE and ADVANCED) Modeling phenomena with complex data
(4) (INTERMEDIATE and ADVANCED) Modeling with selected transient nonlinear Differential Equations.
Prerequisites: Linear Algebra and Differential Equations.
Sunday April 27 0900-12:00
Location: Oregon State University, Kidder Hall
09:00-09:30 breakfast
09:30-12:00 Minitutorial II
Title: Mathematics of Unsupervised Learning Methods
Goal: This tutorial focuses on the mathematics underlying unsupervised learning methods, including PCA, Diffusion Maps, and t-SNE. Setting up an appropriate Python environment for implementing and applying these methods to sample data sets will be covered in the tutorial.
(1) (BASIC) loading and manipulating data
(2) (INTERMEDIATE) running and interpreting PCA
(3) (ADVANCED) Using Diffusion Maps and t-SNE
Prerequisites: Linear Algebra and basic programming in Python.
Registration of the Minitutorials is now closed.