About Me

I am a currently a Research Fellow at the Sleep Medicine Division at Brigham and Women's Hospital

My focus is on epidemiological studies and methodologies using genomics, metabolomics, and proteomics.

My current research at the Sleep Medicine Division at Brigham and Women's Hospital and Harvard Medical School focuses on utilizing Omics for epidemiological studies on sleep disorders, such as Excessive Daytime Sleepiness (EDS), and health outcomes associated with sleep disorders.

I received a scholarship for my PhD candidacy at the Department of Clinical Epidemiology, LUMC, Netherlands. My PhD thesis, "Multi-Omics in Research: Epidemiology, Methodology, and Advanced Data Analysis", focuses on using multiple Omics to study a variety of ailments. These Omics include genomics (Genome Wide Association Studies, Genomic Risk Scores), Metabolomics (large data from UHPLC-MS/MS and NMR platforms), and proteomics.

  • Worked as a visiting researcher at the University of Cambridge (Department of Public Health and Primary care). It was a cooperative effort to perform an analysis for one of my ongoing projects on “metabolomics aging and disease prediction”.
  • Besides our direct projects we are also working on secondary projects with The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.
  • Organized and prioritized work to complete assignments in a timely, efficient manner.
  • Experienced in teaching courses for medical students. Courses include basic epidemiology, study designs, and mendelian randomization.

Career and Education

    From Saudi Arabia to England, to the United States, and to the Netherlands

  • 01/2023- Present : Research Fellow at Brigham Women's Hospital & Harvard Medical School
  • 08/2018- 03/2023 : PhD candidate at Leiden University Medical Center
  • 10/2013 - 08/2018: Bioinformatician • King Faisal Specialist Hospital and Research Center
  • 01/2012 - 06/2013: MSc Bioinformatics • 2013 • Georgetown University, Washington, D.C., USA
  • 09/2007 – 07/2011: BSc Human Genetics • 2011 • University of Leeds, Leeds, UK
  • International experience and network
  • Adaptability
  • Fast Learner
  • Critical thinking and problem solving
  • Good teamwork, communication skills and ability to participate in research communities across organizational units.
  • Background in Epidemiology on cross sectional and longitudinal cohorts
  • Background in computer science and bioinformatics
  • Worked with genomic, proteomics, and metabolomics
  • Experienced in Big Data Analysis
  • Experienced in Missing Data Analysis
  • Backgound in Genetics and working in the lab
  • Proficient in Python and R programming languages
  • Worked using Jupyter Notebook and R Studio
  • Worked with STATA and SPSS
  • Used several HPC clusters for big data analysis
  • Worked with database resources such as SQL and Django
  • Familiar with Git/GitHub, HTML5, Bash, Adobe Photoshop, Adobe Illustrator and Blender
  • Experienced with Windows, MacOS and UNIX operating systems
  • Portfolio

    My projects revolve around the usage Multi-Omics to study a variety of phenotypes
    Here you can find projects on different topics that involved genomcis, proteomics, and metabolomics (and more).


    Multi-Omics in Research: Epidemiology, Methodology, and Advanced Data Analysis

    Genome-wide association analysis of composite sleep health scores in 413,904 individuals
    Metabolomic Profile of Excessive Daytime Sleepiness

    Multi-ancestry GWAS improve resolution of genes and pathways influencing lung function and COPD risk
    Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts
    A Workflow for Missing Values Imputation of Untargeted Metabolomics Data

    Agreement of aptamer proteomics with standard methods for measuring venous thrombosis biomarkers
    Hepatic triglyceride content is intricately associated with numerous metabolites and biochemical pathways
    Normal range CAG repeat size variations in the HTT gene and their assoction with lipoproteins profile
    PFAS concentrations are associated with an unfavorable cardio-metabolic risk profile

    Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites

    The lipid profile for the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis: The HOPE study

    Saudi Human Genome Project