My name is Babak Aslani, and I am a Postdoctoral Researcher at the Department of Systems Engineering and Operations Research at George Mason University. I hold a Ph.D. in Systems Engineering and Operations Research from George Mason University. I combine research, teaching, and service to advance systems optimization and data-driven decision science. I develop data-driven methods in optimization, machine learning, and decision science to tackle complex societal challenges, from urban infrastructure to safety-critical systems. I am passionate about research, mentoring students, teaching courses in data analytics and operations research, and translating research into projects with broad impact.

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.

Machine Learning (ML) provides a promising approach to enhance optimization algorithms across various problem settings, including discrete and continuous, deterministic and stochastic.

Constructing approximations to reduce computational effort or guiding the search toward high-quality solutions.

Scalable optimization for large-scale networks in diverse applications like interdependent infrastructures, supply chains, and healthcare analytics.

Learning-based solution algorithms for mixed-integer programming (MIP) and Quadratic Programming(QP) models.

Aims to develop scalable methods for discrete/continuous and deterministic/stochastic optimization in different application areas.

Data-driven decision support systems (DSS) have become indispensable for addressing complex and uncertain problems across engineering, healthcare, and policy domains.

Integrate data analytics, optimization, and machine learning to enable evidence-based, adaptive, and predictive decision-making.

Develop robust, interpretable, and scalable DSS frameworks to improve the trustworthiness and effectiveness of decision-making across critical domains such as infrastructure, healthcare, and social systems.

Adaptive DSS capable of continuous learning and decision-making from streaming or cloud-based data.

Embedding interpretable ML models into DSS to ensure transparency, fairness, and human trust.

Expanding data-driven digital twin platforms to enable predictive maintenance, resource allocation, and resilience enhancement in interdependent infrastructure systems.

Current and Future Research