The Business School

Weixiao Huang third-year PhD student in Data Science, presented Combinatorial Exchange for Resource Sharing among Nonprofits, which introduces a novel auction-based platform that facilitates nonprofits to exchange both physical resources as well as the time and talents of human resources. She discussed results from a real-world deployment of the technology in Maryland.

Ryan Killea, second-year MS student in Data Science, was happy to have attended INFORMS with ARCHES for the first time and took the opportunity to network and attend talks related to his research interests in mixed integer/combinatorial optimization and public sector operations research.

Shima Azizi, Assistant Professor of Business Analytics at St. John’s University, presented A Time-Space Network Approach for the Foster Care Visitation Scheduling Problem, where she discussed a novel network-based optimization approach to improve the consistency of foster care visits and quality of life for foster children, while assisting foster care organizations to better operationalize their resources.

Marcela Vasconcellos, first-year PhD student in Data Science, presented Matching Refugees to Stable Employment Opportunities via Many-to-Many Optimization, a project that matches refugees to stable job opportunities using integer optimization. The model is integrated into an employment platform, scaling the efforts of a refugee job placement agency in Mexico City, while improving the expected match quality of participating refugees and employers.

Andrew Trapp, Associate Professor of Operations and Industrial Engineering, presented Risk-Averse Placement Optimization in Refugee Resettlement, which explores explicitly factoring in risk associated with refugee placement recommendations to accommodate for the lack of certainty in outcome estimation, thereby allowing for recommendations that hedge against unfavorable outcomes.

Forrest Miller, senior BS student in Mathematical Sciences and Data Science, presented Optimizing the Benefit-to-Cost Ratio for Effective Capacity Deployment for New York City’s Homeless Youth Shelter System, which investigates finding the optimal marginal deployment for new shelter locations and resources in New York City for runaway and homeless youth. The project is in collaboration with the New York City Mayor’s Office and directly informs decision makers there.

Fatemeh Farajzadeh, second-year PhD student in Data Science, presented Proactive Staging of Border Resources in a Secure and Humane Manner via Stochastic Programming, which uses stochastic optimization methods to position scarce humanitarian border resources under demand uncertainty that allows for optimal redistribution (sharing) decisions. She used US-Mexico aggregated monthly Customs and Border Patrol (CBP) data for the past three years to provide solutions to post-pandemic migration crises.   

Geri Dimas, fifth-year PhD student in Data Science, presented Modeling the United States Immigration Court System: An Application of Simulation and Data Science, research that models the intricacies of the US Immigration Court System, seeking system improvements to address the nearly 2 million case backlog in an efficient and equitable manner.