Research

Overview

Data driven machine learning. Modeling and discovery of dynamical systems.

Foundation Models for Multiscale Digital Phenotypic Data Streams

Adapting and applying dynamical systems learning in foundation models for wearable sensors.
Sparse Identification of Nonlinear Dynamics

Developing a new model learning architecture inspired by the widely used (two-step) Sparse Identification of Nonlinear Dynamics (SINDy) that learns a model in scenarios of extremely scarce or noisy data measurements.
Reduced Order Modeling for Plume Dynamics

Developing fast reduced order modelings for characterizing plume dynamics directly from video data.

A joint optimization approach to identifying sparse dynamics using least squares kernel collocation. 2025 [arXiv]
Alexander W. Hsu, Ike Griss Salas, Jacob M. Stevens-Haas, J. Nathan Kutz, Aleksandr Aravkin, Bamdad Hosseini

Data-driven coarse-graining and reduced-order modeling of plume ejection dynamics from video observations. Physics of Fluids 37, 107145 (2025) [arXiv][Journal]
Ike Griss Salas, Megan R. Ebers, Jake Stevens-Haas, J. Nathan Kutz