Bobcat TAILS

Leafy Spurge (Euphorbia virgata) is an invasive species to Montana. The pop detection project was intended to explore ways to detect the audible "pops" made by its exploding seed pods. Students useda wavelet scattering transform as inputs to an autoencoder. A public github repo for the Leafy Spurge audial detection project is available here. 

The card game, SET,  consists of 81 unique cards that vary in four features across three possibilities for each kind of feature. The computer vision project aims to create a neural net capable of identifying the four characteristc features of a given card from a photo. Future work aims to expand on the identification of cards to the identification of valid sets. 

Flow cytometry fluorescence data were collected to evaluate the performance of a synthetic biology logic circuit embedded in the yeast genome (Cummins et al., 2023). The analysis is dependent on well-separated normal (Gaussian) distributions of low and high fluorescence, but the data, including the controls, were found to be bimodal. This project aims to improve the separation using a Gaussian mixture model (GMM) to separate the bimodal distributions of the control groups and ultimately classify the experimental conditions. Pipette Image Source

This project aims to predict high-resolution spatial Evapotranspiration (ET) data within an agricultural field by addressing both temporal and spatial information gaps. Three in-field sensors provide daily ET data at specific locations within the field, while weekly/biweekly aerial missions capture spatial ET data but only at discrete time points. We seek to fill spatio-temporal gaps using statistical and machine learning methodologies.To accomplish this objective, we employ a low-rank tensor approximation framework to model the spatiotemporal ET data. Leveraging the available data, we apply various solvers, including Gradient Descent, BiasMF, FunkSVD, Genetic Algorithm, among others, to predict missing values within the modeled spatiotemporal ET matrix. 

LogLAI is an R package that is currently in progress!  It contains functions created by John Smith that use Lognormal State Space Functions to project leaf area index and carbon sources in forests.  These commands take in synthetic/real data, use them to create particle filters, and these particle filters are then used to create objective functions, which contain marginal loglikelihood and trajectory of the carbon sources we were keeping track of.  A large amount of these commands were built in pomp, which lets us interface particle filter implementations in C, making them run faster! The LogLAI package can be accessed here and can be installed in R after installing devtools with the command: install_github("turnerch/loglai")

Clustering is a common unsupervised machine learning approach that looks to identify groups within unlabeled data. The identification of groups is a critical aspect of biological studies that are used to identify potential biomarkers of disease. These potential biomarkers of disease are often used to drive future studies of a disease, thus, performing valid clustering of the data is critical. In this study, we investigate three clustering algorithms fuzzy c-means, k-means, and gaussian mixture models and the impacts of hyperparameter selection on clustering results. Furthermore, we will demonstrate how the application of each of these algorithms impacts the discovered clusters within biological data, and ultimately impacts future biological directions.

This project aims to use various machine learning algorithms specialized in identifying nonlinear dynamics such as SINDy, sparse model selection via integral terms, and a neural net, to find models from data.  One goal is to model the friction forces on projectiles moving through air.  A second goal is to build a forced Van der Pol circuit and identify a working model through the algorithms.