About Me
I’m a PhD candidate in Genetics at Massey University, Auckland, investigating RNA modifications in Escherichia coli using Oxford Nanopore direct RNA sequencing. My research decodes how epitranscriptomic marks influence bacterial gene regulation—work that requires both hands-on molecular biology and advanced computational analysis.
Current Research
My doctoral work centers on the E. coli epitranscriptome—mapping RNA modifications across diverse natural isolates to understand their functional roles in bacterial adaptation. Before this, I spent time at Alfred Health, Melbourne as a Research Scientist, tracking carbapenem-resistant pathogens through genomic epidemiology and whole-genome sequencing analysis for clinical and public health applications.
Research Journey
My path has wound through pharmaceutical R&D (cancer drug discovery), neuroscience (Alzheimer’s mechanisms), and now microbial genomics. The common thread: starting with a biological question, then building or adapting the methods needed to answer it.
The shift to computation happened by necessity during my PhD. Nanopore sequencing produces data that traditional analysis tools can’t handle—so I learned Python, Unix workflows, and pipeline development with Nextflow. This dual perspective—understanding both how the data is generated and how to extract meaning from it—shapes how I approach every project.
Professional Background
Technical Expertise
- Python (data analysis, automation, Biopython)
- Nextflow (workflow management, HPC environments)
- Oxford Nanopore sequencing (library prep, signal analysis)
- Comparative genomics, phylogenetics, AMR surveillance
- Molecular techniques (nucleic acid work, qPCR, microbial culture)
- Unix/Linux systems, version control
- 10+ years research experience (academia, biotech, clinical)
Research Domains
Current focus: Microbial genomics, RNA biology
Past work: - Antimicrobial resistance genomics - Neuroscience (Alzheimer’s, dementia) - Cancer biology (drug discovery) - Infectious disease diagnostics - Regenerative medicine - Endocrinology - Lab-on-a-chip technology
Philosophy
Good computational biology starts in the lab. Understanding how data is generated—sample prep, sequencing chemistry, potential artifacts—fundamentally changes how you analyze it. I prioritize reproducible workflows, clear documentation, and choosing the right tool for the biological question, not just the computational convenience.