Biography
I am an evolutionary biologist with a particular interest in the molecular evolution of toxin proteins. I completed my PhD at East Carolina University with Michael Brewer as my advisor. For my dissertation research, I developed bioinformatics and deep learning implementations to identify and characterize venom proteins in wandering spiders. I am currently a postdoctoral researcher at the University of Texas at Austin, where I am working with Clause Wilke in collaboration with Bryan Davies to develop bioinformatics and deep learning techniques to characterize and design antimicrobial peptides.
Interests
- Spider venom evolution
- Bioinformatics
- Molecular evolution
- Genomics
- Deep learning
Education
PhD in Biology, 2021
East Carolina University
BS in Biology, 2016
Samford Universit
Experience
Postdoctoral fellow
Implement bioinformatics and deep learning techniques to characterize and design antimicrobial peptides
Graduate Teaching Assistant
Taught introductory laboratory for undergraduate Biology majors.
NSF Graduate Research Fellow
Conducted research on the molecular evolution of venom proteins in wandering spiders.
Publications
(2020).
Pick Your Poison: Molecular Evolution of Venom Proteins in Asilidae (Insecta: Diptera).
Toxins.
(2020).
Ultraconserved elements help resolve the phylogeny of an ancient radiation of venomous flies (Diptera: Asilidae).
bioRxiv.
(2016).
Prescribed Burning Affects a Measure of Fitness in Ctenus hibernalis (Araneae: Ctenidae) at Oak Mountain State Park, Shelby County, AL.
Southeastern Naturalist.
Accomplishments
Natural Language Processing in TensorFlow
Built natural language processing systems using TensorFlow. Processed text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. Applied RNNs, GRUs, and LSTMs in TensorFlow. Trained an LSTM on existing text to create original poetry.
See certificateConvolutional Neural Networks in TensorFlow
Worked with real-world images in different shapes and sizes, visualized the journey of an image through convolutions to understand how a computer “sees” information, plotted loss and accuracy, and explored strategies to prevent overfitting, including augmentation and dropout. Used transfer learning to extract learned features from models.
See certificateIntroduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Best practices for using TensorFlow, a popular open-source machine learning framework.
See certificateTinyML1: Fundamentals of TinyML
Basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models.
See certificate