Welcome to my page!
Hello! My name is Sara Ichinaga and I am an Applied Mathematics Ph.D. candidate at the University of Washington studying under Professors J. Nathan Kutz and Steven L. Brunton. I study, implement, utilize, and extend data-driven methods that can be used to model time-varying data sets (i.e. time-series data, video data, etc.) for tasks like dimensionality reduction, future state prediction, system control, and learning governing equations from data.
Highlights
Here are some things that I’ve been working on recently!
PyDMD: A Python package for Dynamic Mode Decomposition
The dynamic mode decomposition (DMD) is a data-driven algorithm that takes time-varying snapshot data sets and decomposes them into their dominant spatiotemporal components. PyDMD is a Python package that implements DMD and many of the algorithm’s most notable variants. We recently revamped the package to include even more variants and features! Read about it in our JMLR paper and our longer version on arXiv. Try out the code on your own data set today!
mrCOSTS: a method for discovering spatiotemporal components in multiscale data
The multi-resolution Coherent Spatio-Temporal Scale Separation (mrCOSTS) algorithm is a methodological extension of the DMD algorithm that consolidates the results of multiple DMD fits that are taken over sliding windows of data across various window sizes. The resulting algorithm is capable of decomposing noisy real-world multiscale data sets. This work has recently been accepted by PNAS! Yay! The paper is available here.
Graduate Certificate in AI and ML for Engineering program
I am currently serving as the (very first!) teaching assistant for ENGR 515 (Data-Driven Optimization), which is a part of the new Graduate Certificate in Artificial Intelligence and Machine Learning for Engineering program offered by the UW College of Engineering. I also proudly served as the teaching assistant for ENGR 510 (Foundations of Machine Learning for Engineering) in Fall 2024.
Contact
Email: sarami7@uw.edu