My name is Babak Aslani, and I am a Postdoctoral Researcher at the Department of Medical Physics at Memorial Sloan Kettering Cancer Center. I received my Ph.D. in Systems Engineering and Operations Research from George Mason University. My research interests include optimization, machine learning, and multi-criteria decision-making. My research has been published in reputable peer-reviewed journals such as Computers and Operations Research, Computers and Industrial Engineering, International Transactions in Operational Research, Journal of Industrial Information Integration, and Expert Systems with Applications.
Latest News
I have been awarded the Iranian American Academics and Professionals (IAAP) flagship Scholarship 2024.
Our paper titled "Nested Learn To Branch-and-Price for Large-scale Network Optimization Problems" has been selected as the Best Paper Award for Operations Research (OR) Track at the IISE Annual Conference, 2024.
I have been awarded IISE Fellows William Biles, Hamid Parsaei, and Victor Zaloom Endowed Scholarship for the 2023-2024 academic year.
Our paper titled "Designing Agent-based Simulation to Assess the Impact of Coordination Structures on Infrastructure Networks Resilience" has been selected as the Best Paper Award for Modeling & Simulation (M&S) Track at the IISE Annual Conference, 2023.
Featured Research
Scalable Learn-to-Optimize Frameworks for Networks: Models and Applications.
Research on combinatorial optimization problems is vital because of their broad popularity in real-world application areas, including transportation, logistics, production, and resource allocation.
Large-scale networks can adequately capture the structure of complex systems containing thousands of nodes (e.g., physical components) and edges (e.g., mutual interactions).
leveraging valuable information during the search process to embed well-informed machine learning methods in optimization algorithms has emerged as a promising research area.
Selected Publications
A systematic review of optimization methods for recovery planning in cyber-physical infrastructure networks: Current state and future trends [Read more]
Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization [Read more]
Current and Future Research
Interpretable Machine Learning and Feature Selection
Nested Learn-to-Branch-and-Price for Large-scale networks
