Abdullah Abdelaziz

Hi! My name is Abdullah. Nice to have you on my blog😁. I am currently a Ph.D. candidate at the Department of Pharmacy Systems, Outcomes, and Policy (PSOP) at the University of Illinois at Chicago. Before starting my Ph.D., I got a pharmacy degree from Minia University, Egypt. Afterwards, I worked as a demonstrator\teaching assistant for about a year and a half, and I REALLY like teaching!

Since I was in pharmacy school, I have been captivated by the world of quantitative methods and playing with large data to get meaningful insights into solving real-world problems, so I decided to pursue a career that combines my passion for health and healthcare and data science.

My early research focused on pharmacy practice. After I started my Ph.D., I have been involved in many research projects on different topics: comparative effectiveness and safety research, drug utilization, adherence, pharmaceutical policy and pharmacy practice, economic evaluation, and evidence synthesis. I worked as a pharmacoepidemiology intern at Regeneron (Summer 2022) under the supervision of Dr. Christian Hampp. Now, I work as a research assistant at PSOP working on my dissertation. I am interested in oncology and infectious diseases as clinical areas, but I am open to other clinical areas as well.

I am a HUGE R fan. In fact, I owe the open-source world big time. When I started learning, I could not afford to purchase any of the known statistical software, purchase expensive textbooks, or attend expensive courses. The open-source world, among other factors, shaped how I think, learn, and even teach.

This blog is a form of paying forward what the open-source world gives me. I will be forever grateful!


  • Ph.D. Candidate at the Department of Pharmacy Systems, Outcomes, and Policy at the University of Illinois at Chicago.
  • BPharm, Minia University, Egypt


  • Epidemiology (Pharmacoepidemiology) and Biostatistics.
  • Oncology and Infectious diseases
  • Pharmaceutical policy and pharmacy practice.
  • Causal inference.
  • Machine learning.
  • Big Data (Healthcare databases).
  • Statistical programming (, , and SAS).