Abdulrahman is a Ph.D. student in the industrial engineering department at the University of Pittsburgh. His current research focuses on devising efficient techniques for estimating treatment effects from large-scale simulations (i.e., simulation metamodel), particularly in scenarios where sampling from these simulations proves to be resource-intensive. Previously, he worked on developing reinforcement learning methods for a financial problem called optimal execution problem, where the decision maker needs to devise a cost-saving strategy to maximize the gain from the execution. He got his MSc and BSc in operations research and computer science from Cairo University.
Google Scholar Link : https://scholar.google.com/citations?hl=en&authuser=1&user=kf8by4MAAAAJ
Research Interest
Metamodeling, Simulation Optimization, Large-scale Simulation, and Machine Learning.
Publications
- "Optimized Model Selection for Estimating Treatment Effects from Costly Simulations of the US Opioid Epidemic", with M. A. Rahimian, and Mark S. Roberts. To be presented in the 2024 Annual Simulation Conference (ANNSIM'24). [arXiv:2403.15755][code]
- "Inferring Epidemic Dynamics Using Gaussian Process Emulation of Agent-Based Simulations", with M. A. Rahimian, and Mark S. Roberts. Winter Simulation Conference (WSC), December 10-13, 2023, San Antonio, TX. [IEEE:10408157][arXiv:2307.12186][code]
- "Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder", with M. A. Rahimian, and Mark S. Roberts. IEEE International Conference on Biomedical and Health Informatics (BHI), October 15-18, 2023, Pittsburgh, PA. [IEEE:10313496][arXiv:2308.13040][code]
- "Optimizing Stock Market Execution Costs Using Reinforcement Learning", with A. Ghoneim, and M. Saleh. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), December 1-4, 2020, Canberra, Australia. [IEEE:9308153]
- Pre-Print: "Computationally Efficient Estimation of Localized Treatment Effects in High-Dimensional Design Spaces using Gaussian Process Regression" [link]
