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.

Featured Research

Learn-to-Optimize Portfolio

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

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.

Recent Publications

A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience [Read more]

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]

Learn to decompose multiobjective optimization models for large‐scale networks [Read more]

Current and Future Research

Drone as First Responder (DFR) for Northern Virginia Emergency Response System (NVERS)

Interpretable Machine Learning and Feature Selection

Nested Learn-to-Branch-and-Price for Large-scale networks

Get in touch at baslani@gmu.edu

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