I'm a bioinformatician who works with mass spectrometry data. My PhD was dedicated to developing computational methods for glycomics and glycoproteomics, finding integrated-omics strategies to dig deeper into complex biological samples. My professional work has involved using that knowledge to analyze immunopeptidomics data to help develop better vaccines for personalized cancer immunotherapy and multi-strain infectious disease protection, using a combination deep learning, population sampling, and sequence conservation models.

📧 Email me if you want to discuss MS data processing techniques, glycoinformatics, or antigenomics

I am actively working on migrating much of the I/O and signal processing code I have written in Python/C to Rust

• mzdata - The I/O and spectrum data layer, including some fancy iterators and convenience functions for reading and writing mass spectrometry data files.
• mzpeaks - Types and traits for generic representation of peaks and features for mass spectrometry, including searchable collections and spatial data structures.
• mzsignal - Algorithms for low-level signal processing of mass spectra and temporal traces like chromatograms or mobilograms, including peak picking, feature extraction, signal averaging, smoothing and denoising.
• mzdeisotope - Algorithms for charge state deconvolution and deisotoping of mass spectra and feature maps.
• mass-fragment-index - Data structures for large-scale searching of precursor-product collections suitable for fragment indices, spectral libraries, or similar data. Includes fast-to-search on-disk serialization.

I am also experimenting with adapting some ML/DL methods from regular proteomics to glycoproteomics.