Research Focus: Design and development of advance machine-learning models for MS data
[Method Development] ML Ecosystem for Mass Spectrometry Data
Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification
Making MS Omics Data ML-Ready: SpeCollate Protocols
Description of Dissolved Organic Matter Transformational Networks at the Molecular Level
Unsupervised structural classification of dissolved organic matter based on fragmentation pathways
Systems and methods for measuring similarity between mass spectra and peptides
Molecular level characterization of DOM along a freshwater-to-estuarine coastal gradient in the Florida Everglades
SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey
Graph Theoretic Approach for the Analysis of Comprehensive Mass-Spectrometry (MS/MS) Data of Dissolved Organic Matter
Parallel sampling-pipeline for indefinite stream of heterogeneous graphs using OpenCL for FPGAs
Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs