About
"From identifying proteomics biomarkers for disease to leading multi-omics biomarker platforms — the question hasn't changed, only the scale has."
Career Journey
From Bench to Bytes
My scientific journey began in proteomics, when I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry. This led me to pursue PhD-level research in mass spectrometry-based proteomics — working at the intersection of analytical chemistry and computational biology to identify biomarkers, decode protein function and protein-protein interaction networks.
Over the years I worked across the full breadth of proteomics — from disease biomarker discovery in serum and plasma, to cellular proteomics, protein-protein interaction studies and PTM-specific platforms spanning phosphorylation, glycosylation and cysteine modifications. I also designed and taught practical proteomics data analysis courses, translating complex computational workflows into accessible training for the next generation of researchers.
Today, I channel that foundation into patient-data driven healthcare analytics, applying scientific and clinical domain expertise and AI/ML-driven approaches to multi-omics data to drive precision medicine and translational research initiatives.
Deep Expertise
What I Actually Know
- LC-MS/MS experimental design & data acquisition
- Database searching (MaxQuant, Mascot, Proteome Discoverer)
- Label-free and TMTquantification
- Post-translational modification (PTM) analysis
- Protein complex & interaction network mapping
- LLM prompt engineering & pipeline integration
- ML model application for biological data
- AI-assisted biomarker candidate scoring
- Data curation and ETL for omics workflows
- Python & R for ML pipelines
- Evaluation frameworks for model outputs in science
- Multi-omics data analysis
- Statistical analysis for high-dimensional biological data
- Differential expression & enrichment analysis
- Data visualisation
- Reproducible pipeline development in R & Python
- Biomarker module architecture & product management
- Cross-functional team leadership (science × engineering)
- Scientific communication for non-technical stakeholders
- Agile delivery in research-product environments
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