I am a bioinformatician with an eclectic background. My academic journey included mathematics, philosophy, biology, teaching, veterinary medicine and public health, all of which have lent to a solid foundation for bioinformatics and computational biology.

My research is grounded in the principles of Open Science, with a strong emphasis on producing computationally reproducible results and reusable, open-source code. Experience working with a broad range of data types has lead me to a pragmatic, "right tool for the job" approach. For data analysis and visualization R is my "go-to", while I leverage the power of the tensor via Python for specialized tasks such as building machine learning pipelines. Leveraging the "multilingual" modalities of Quarto and Shiny, I create dynamic presentations and exploratorytools that can be used to communicate findings.

In parallel with the "right tool for the job" mentality flows an undercurrent of "right data for the question". The background in experimental design and methodologies provided by concentrating my MPH on biostatistics and epidemiology adds to the "particular set of skills" I bring to aligning data with the research question at hand. Collectively, these elements are supported by my passion for data visualization and science communication and fueled by an intense curiosity.

Within these repositories, you will find projects I have worked on: course projects, thesis work, and the current projects I am able to post..
Aggregating my projects here and organizing the appropriate documentation is a work in progress.

In addition to those projects, there is also the CubEd project worked on with "my" lab, the Grunwald Lab, at UMass Chan Medical School. I created the GitHub page for the site using Quarto, with visual elements drawn from the Classroom Manual and Assembly Guide, which were written in collaboration with Colton Hormann. Colton Hormann was responsible for all of the 3D graphics and rendering. I created the layout for the pdf versions using Adobe InDesign and created the graphic elements using Adobe Photoshop and Illustrator.

Currently, I am working on an pipeline for processing and analyzing single-molecule tracking data applying neural networks and likelihood estimation to segmentation and particle tracking, which we are using to study mRNA transport through the nuclear pore complex in yeast.

I also have experience with NGS data such as bulk RNA-seq, Hi-C, and DamID data on their own as well as in combination ("multi-omics") and in conjunction with datasets drawn from databases such as OpenTargets.

My work has been done locally, on both Windows and Mac, as well as on high-performance computing clusters using command-line tools.

My graduate-level coursework included applying machine-learning techniques to LIDAR and fNIRs datasets as well as modeling protein-ligand interactions with tools such as Chimera and Rosetta.

My experience also includes work in public health as a data scientist with the MDPH where I studied:

• Immunization rates among young children using Massachusetts Immunization Information System (MIIS) EHR data using SAS

• Lyme disease surveillance in an endemic state using Massachusetts Virtual Epidemiologic Network (MAVEN) laboratory and case data using SAS and R

• Policy and regulations applied to traveling animal exhibits such as those found at fairgrounds and brought to schools for educational purposes to make recommendations considering the potential of such activities as potential sources of disease.