I am a data scientist with experience developing image processing, segmentation, and classification tools for image data. I completed my Ph.D. in Medical Physics at the University of Chicago in December 2021. My work there involved developing computer vision and image registration tools for validation studies between multiple preclinical imaging modalities including synchrotron x-ray microCT, diffusion MRI tractography, fluorescence microscopy, and electron microscopy. Now at Waters Corporation, I work as a Data Engineer developing machine learning tools and software for processing and exploring mass spectrometry imaging data. In my free time, I can usually be found watching something from the Criterion Channel, playing guitar, running, rock climbing, or baking bread.
Python package for performing analysis on structural brain networks using random geometric surrogate graphs.
Python package for extracting and visualizing the orientations of local structures in 3D imaging data using structure tensor analysis.
Personal side-project for the segmentation of cell nuclei in multiple optical imaging modalities. Model built with TensorFlow using a UNET architecture, app built and deployed with Streamlit.
Data Engineer, January 2022 - Present
Graduate Research Assistant, September 2016 - December 2021
Undergraduate Research Assistant, January 2013 - May 2016
I attended the ASMS conference in Minneapolis.
I received the Best Thesis award from my graduate program.
My latest manuscript uses model-free analysis of echo-planar spectroscopic imaging to show that fitting data to common biophysical models lowers myelin classification performance.
I have been hired as a Machine Learning Intern at Waters Corporation, where I will be developing python web apps for mass spectrometry imaging.
I successfully defended my thesis entitled "Multi-modal validation of MR microstructure imaging in the mouse brain.
Python (NumPy, Pandas, Scikit-learn, Matplotlib, Bokeh, Keras, TensorFlow, PyTorch), Bash, MATLAB, SQL, R, C++
Computer vision, machine learning, image segmentation, network analysis
git, GNU Emacs, LaTeX, Jupyter, Docker, AWS