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About me

I am an interdisciplinary researcher working at the intersection of microbial genomics, antimicrobial resistance (AMR), infectious-disease epidemiology, and machine learning. Since 2024, I have worked in Professor Danesh Moradigaravand’s Laboratory of Infectious Disease Epidemiology at KAUST, where I link bacterial genomic variation to clinically and biologically important phenotypes using population-genomic, bioinformatic, and machine-learning approaches.

My research interests include genomic epidemiology, population genetics, infectious diseases, AMR, genotype-phenotype prediction, explainable AI, and evidence-grounded generative AI for clinical decision support. My earlier research applied machine learning to environmental AMR and wastewater-treatment problems.

Current research

• Predicting mortality, ICU admission, and length of stay from bacterial genomes and clinical metadata.
• Mapping genome-environment fitness landscapes using chemical genomics, bacterial GWAS, and machine learning.
• Genomic surveillance of clinical and environmental antimicrobial-resistant bacteria in Saudi Arabia.
• Evaluating AI-assisted antibiotic prescribing through structured clinical vignettes.

Selected projects

• Saudi Pathogen Atlas — an interactive genomic-surveillance dashboard for antimicrobial-resistant bacterial isolates in Saudi Arabia.
• GenoPredict — a genome-based clinical-severity prediction platform using interpretable machine learning.

News

• Two 2026 preprints report genomic prediction of clinical severity and high-dimensional genome-environment fitness landscapes in Klebsiella pneumoniae.
• Our national genomic study of methicillin-resistant Staphylococcus aureus was published in Frontiers in Microbiology in 2025.
• Research on probabilistic prediction of phosphate adsorption by biochar was published in Chemosphere in 2025.