I am a bioinformatician passionate about leveraging computational genomics to tackle one of the greatest global health challenges: Antimicrobial Resistance (AMR). I specialize in building robust pipelines and applying machine learning models to analyze large-scale biological data, aiming to uncover insights into pathogen evolution and drug discovery.

My work applies computational methods to key areas of infectious disease research:

• Genomic Surveillance: Analyzing pathogen genomes to track the spread of resistant strains.
• Host-Pathogen Interaction: Using transcriptomics (like scRNA-seq) to understand cellular responses to infection.
• Computational Drug Discovery: Applying machine learning to identify novel enzyme targets and predict function.

Here are some projects that demonstrate my skills in pipeline development and machine learning for biological data analysis:

• SEPI 2.0: Antibiotic Sensitive Efflux Protein Identifier

  A versatile bioinformatics platform for the automated acquisition of reference protein sequences from NCBI. SEPI 2.0 empowers researchers to build high-quality, custom reference datasets for any bacteria and any protein set with minimal effort and maximum reproducibility.

• SubScan: Polymorphisim Finder

  SubScan is a lightweight Python tool designed to extract amino acid substitutions from EMBOSS alignment files. Built with Biopython and ready to run in Google Colab, it simplifies comparison between protein sequences—especially for AMR gene variation studies across lab isolates