Research

Research programme

My research examines how microbial genomes, multi-omics signals, and computational models can be integrated to resolve infectious disease biology with greater precision. I am particularly interested in antimicrobial resistance, pathogen adaptation, comparative genomics, and data-driven approaches that connect molecular complexity to clinically relevant biological insight.

Core orientation

Infectious disease biology

Computationally oriented research focused on pathogen biology, genomic variation, and translational interpretation.

Analytical frame

Multi-layer inference

Integrating genome-scale information across multiple biological levels to move from description toward mechanism.

Application space

AMR and precision microbiology

Emphasis on antimicrobial resistance, host–pathogen systems, biomarker discovery, and computational prioritization.

Scientific direction

Across these themes, my aim is to develop analytically rigorous frameworks that clarify how genomic content, systems-level regulation, and computational evidence can be combined to interpret pathogen behaviour in clinically and epidemiologically meaningful ways. This includes questions of resistance evolution, strain-level diversity, biomarker discovery, and the identification of mechanistic signatures that may otherwise remain hidden when data layers are analysed in isolation.

Theme 1

Multi-omics integration and systems-level disease biology

I use integrative omics approaches to investigate infectious disease processes beyond single-data-layer interpretation. By combining genomic, proteomic, transcriptomic, and related molecular information, I seek to identify biomarkers, define host–pathogen interaction signatures, and reconstruct system-level responses that connect genotype to biological function.

Biomarker discovery Host–pathogen signatures Systems biology Network analysis

Theme 2

Microbial genomics, comparative genomics, and pangenome structure

My genomics work focuses on how microbial populations diversify, adapt, and circulate across clinical and environmental settings. I am interested in whole-genome sequencing, comparative genomics, pangenome analysis, and the study of resistance-associated and mobile genetic elements that shape pathogen evolution and lineage-specific risk.

Whole-genome sequencing Pangenomics AMR genomics Mobilome analysis Phylogenomics

Theme 3

Computational drug design and target prioritization

I apply in silico strategies to identify and prioritize candidate targets and compounds against microbial proteins of biological and therapeutic importance. This includes structure-guided reasoning, docking-based workflows, and computational screening approaches that help refine hypotheses before experimental validation.

Molecular docking Target discovery Structure-guided analysis In silico prioritization

Theme 4

Artificial intelligence and machine learning in biomedicine

I am interested in how machine learning can support the interpretation of high-dimensional biological datasets, especially in infectious disease research. My focus includes predictive modeling, resistance-associated pattern detection, and computational frameworks that improve the prioritization of biological hypotheses and therapeutic strategies.

Predictive modelling High-dimensional data AI in biomedicine Resistance prediction

Research perspective

I am especially motivated by research questions that require both biological depth and computational discipline: questions where genome-scale data, systems-level interpretation, and translational relevance need to be held together in a single analytical framework. In this sense, my work is not only about describing pathogens, but about developing more precise ways of understanding how they evolve, function, and respond under real biological pressure.