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The complexity of decision-making escalates as data expands and resources decrease. This complexity becomes more critical in societal challenges as decisions are characterized by uncertainty and equity. 

I am a Ph.D. candidate in Data Science at WPI, holding a master's degree in Industrial Engineering from Sharif University of Technology. My focus is on solving operational challenges in systems serving vulnerable populations; to date,  I have explored humanitarian operations and refugee resettlement.  My research methodology centers on using prescriptive (integer optimization) and predictive (machine learning) analytics, together with algorithms, to effectively allocate scarce resources.

My exploration into humanitarian operational challenges began with my master's thesis. I developed a mixed integer programming (MIP) model to optimize allocation and routing decisions within a multi-echelon supply chain. This research laid the groundwork for my continued passion for using optimization for social good. Later, we proposed an MIP model to innovate a last-mile delivery system in metropolitan cities, integrating drones with public transportation systems to avoid traffic and tackle drone operational limitations in emergency operations. 

Currently, my research is on developing a stochastic programming model to proactively allocate resources for migratory crises in borderlands. I have also been leading projects on improving the operational effectiveness of private refugee resettlement, partnering with regional nonprofits—one of which includes a pioneering preference-based matching system, called Refugees Uniting Through HIAS (RUTH),  deployed by HIAS to facilitate the relocation of Ukrainian refugees to the United States.  

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